Modeling and Simulation (M&S) as a discipline requires M&S Science to understand the general principles that build the foundations of M&S as a discipline, M&S Engineering to find general methods and solution patterns that can be applied to various problem domains, and M&S Applications to find real M&S-based solutions to real world problems. It also requires a deeper understanding of its epistemological and computational constraints from its students. The presentation addresses the mathematical foundations, the resulting philosophical implications, and looks into the future potential contributions of M&S in decision support on all levels. One of the main ideas is a paradigm shift from finding and supporting the best strategy towards allowing to take all alternatives into account and avoid bad and unstable alternatives.

Simulation is used increasingly throughout research, development, and planning for many purposes. While model output is often the primary interest, insights gained through the simulation process can also be valuable. Insights can come from building and validating the model as well as analyzing its behaviors and output; however, much that could be informative may not be easily discernible through these existing traditional approaches, particularly as models continue to increase in complexity.

This research extends current work in model analysis and program understanding to assist modelers in obtaining more insight into their models and the systems they represent. Results indicate these tools and techniques, when applied to even modest simulation models, can reveal aspects of those models not readily apparent to the builders or users of the models.

Population-based microsimulations often incorporate multiple data sources and modeling techniques. Results from these complex models should include uncertainty from model inputs. This work examines the confidence of reported results from population-based microsimulations using the Future Elderly Model, a dynamic microsimulation that forecasts health and economic outcomes for middle age and older adults. The primary objective is to determine whether systematic inclusion of uncertainty will disrupt the policy conclusions obtained from population-based microsimulation results.

This research presents a method to build large-scale agent-based modeling and simulation on PC for large-scale social systems with complex social networks. The novelty of this method is reflected in both designing of individual agent and organizing group of agents for interactions on a large scale. As a case study, we constructed a large-scale artificial city Beijing, with which to test policies for controlling the spread of disease among full population(19.6 million) in Beijing.

This project aims to explain why the 2008 crisis was more severe and different. Risk spread so fast that bank failures resembled the dynamics of popping corn unlike a typical cascading avalanche of cause and effect. To model the systemic risk of the 2008 financial crisis, we will assume two interdependent networks, such as the banks network and the housing market network. High correlations in the networks cause shrinking of distance between nodes/networks. It means that a small failure can be transmitted to many nodes in another network fast and easily. Thus, risk can move around between two networks, resulting in an increasingly devastating ping-pong effect. Even if a single network structure is resilient, this kind of risk can make it vulnerable. The key premise of my project is that ping-pong and cascading effects in multiple interdependent networks can explain the observed popcorn effect.

Discrete Event Simulation (DES) is arguably one of the most popular operation research techniques. Even though Commercial-Off-The-Shelf (COTS) DES software has reached an impressive state of maturity, there are still problems that deter organizations from investing in such tools and adopting DES in their decision support processes. On the other hand, the nature of Open Source (OS) software development has properties that would help in overcoming problems such as high cost and lack of flexibility and reusability of DES projects. However, it seems that OS DES has so far achieved limited success, especially in fields like manufacturing, logistics or services. We present a new OS library of manufacturing DES objects called ManPy. We justify the need of such a project and describe the main driving ideas behind it.

Discrete Event Simulation (DES) is one of the most effective tools for planning, designing and improving material flows in production. One of the main weaknesses of operating DES is the exertion needed and costs spent on collecting and handling the input data from different organization’s data resources. To tackle the problem of the time consuming input data process for DES projects a tool, called Knowledge Extraction (KE) tool is developed. The open-source (OS) tool reads data from several resources of an organisation; analyses it using statistical analysis and outputs it in a format that is applicable to be used by a simulation tool. The tool can export the already processed data in formats readable to simulation software in order to cover the increasing need to integrate simulation with other manufacturing applications; the primary one follows the Core Manufacturing Simulation Data (CMSD).

When composing simulations from multiple domains, developers can choose from a long list of possible solutions. However, creating a functional and valid composition from existing simulation building blocks can be cumbersome. Existing solutions are often limited to specific platforms or require extensive and complex implementations. The Coupled and Component-based Simulation (CoCobaSim) approach aims at simplifying the development of an interoperable simulation composition using state-of-the-art model-driven techniques. It uses a component-based approach and employs interaction contracts to define simulation interactions. Based on the modeled information and a chosen simulation platform, developers can choose from several patterns and tactics to generate platform specific interoperability adapters and a suitable execution workflow.

Justifications for obtaining nuclear weapons remain largely unchanged since 1945, and focus primarily on security, economics, and prestige. Texas A&M University developed the modular Bayesian Agent Based Modeling [ABM] Nonproliferation Enterprise (BANE) tool for nuclear nonproliferation analysis. BANE balances intricate policy and technical factors simultaneously. Bayesian and ABM methods are integral for BANE providing nonproliferation assessments. Bayesian inference has been employed in fields such as intelligence, where information limits are ever present. Entities engaged in nuclear proliferation cover a range of activities, but can be broken up into proliferating, defensive, and neutral agent classes. BANE facilitates intelligent agent actions by employing entropy for proliferation pathway determinations. Through BANE the framework exists for expanding beyond the nuclear field into areas such as exploring broader weapons of mass effect (WME) proliferation. Expanding BANE to WME and conventional weapon options will increase policy maker understandings about state trade-offs for securing their national interests.

On February 17, 2009 The Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009 was signed into law, to promote the adoption and meaningful use of health information technology (HIT) (Public Law 111-5 2009). Health Information Exchange (HIE) is one of the focus areas within this act, and there are a number of concerns with its implementation and sustainability; slow adoption rate, cost, benefits to healthcare providers (HCPs), and sustainable business model without government subsidies. This research uses a systems dynamics (SD) simulation modeling approach to map complex relationships among healthcare systems to help policy and decision makers on a strategic level to estimate financial performance over time, and to investigate key factors affecting the HIE adoption rate feedback loops.

This study applied multi agent simulation (MAS) to investigate the impact of farm credit (an agricultural land-use change adaptation strategy) on farm household livelihood. General household (hh) and land use data were obtained from 186 sampled hh. Two hh types were identified; the first hh type (hh-1) was better-off in terms of land area cultivated and income generated from rain-fed rice. Conversely the second hh type (hh-2) was better-off in terms of land area cultivated and income generated from maize. Determinant of crop choices of each hh types (hh-1 and hh-2) were generated via logistic regression, using household and plot characteristics as predictors. Crop choice model and agricultural yield dynamic model was programmed in Netlogo 5.0.5. Using a process based decision, hh choice of maize adoption with respect to maize credit (maize credit scenario) was simulated, and its impact on farm household livelihood was compared with baseline scenario.

New technologies challenge current approaches in most industries, but decisions concerning their adoption often involve evaluation of complex trade-offs and consideration of a large number of alternative choices. This poster considers portable ultrasound machines as an example of a new technology that might be sued to replace or supplement magnetic resonance imaging (MRI) for shoulder disorder diagnosis in orthopedic clinics. When implementing portable ultrasound machines, patient health outcomes need to be considered in addition to costs. A discrete-event simulation model and a simulation optimization algorithm are used to analyze the trade-off between health outcomes and the cost of implementing the portable ultrasound machines. The decisions include purchasing and locating portable ultrasound machines, training of users at appropriate clinics and the MRI capacity allocated for shoulder disorders. The simulation optimization algorithm provides an approximated Pareto optimal set of system designs that allows decision makers to comprehensively understand the trade-offs.

Operating room (OR) rescheduling is the process of adjusting the surgery schedule when the current schedule is subjected to disruptions on the day of surgery. The decision to make a schedule adjustment will impact patient safety, patient satisfaction, hospital costs, as well as surgeon satisfaction. Of particular importance is when, and how frequently, to update the scheduling and tracking systems. These questions and their impact on maintaining schedule accuracy and minimizing room overtime are explored. Discrete event simulation was used to simulate surgical cases in the OR and to test different “right shifting” and case updating policies for their effectiveness. Results and staff experience indicate that ten minutes is the preferred delay in which an update should be made; otherwise staff satisfaction or schedule accuracy will suffer.

Recent advancements in small unmanned vehicles and their capability in collecting dynamic data using onboard sensors make them key players in a wide variety of applications such as mapping, monitoring, search, and rescue. Specifically, monitoring in the border environments using unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs) is the focus of this research work. In this regard, we design, develop, and demonstrate a simulation-based planning and control system for surveillance and crowd control via collaborative operation of UAVs and UGVs through three phases. At the first phase, a dynamic data driven adaptive multi-scale simulation (DDDAMS)-based planning and control framework is designed and developed. Next, in the second phase a testbed is implemented using agent-based hardware-in-the loop simulation. Finally at the phase three, using the developed framework and testbed in previous phases, different control architectures as well as UAV and UGV team formation are addressed.

For colorectal cancer and other chronic diseases, health outcomes may be improved through improved screening of individuals. We wish to optimize the allocation of a limited public health budget to interventions that change individuals’ choice to screen. The effect of these interventions is dependent on the effort placed into them as well the characteristics of individuals to which they are applied. We develop methods to optimize the allocation of a fixed public health budget across individuals’ life course and between different demographic groups. To support this optimization, we develop a procedure that allows us to translate unit levels of public health policy efforts into changes in individuals’ decision making over time. We use a simulation model that incorporates the dynamics of colon cancer natural history and individuals’ screening agency to test and illustrate the effects of the polices developed by our procedure.

This work has investigated four selected topics of the Supply Chain Planning Matrix to deal with uncertainties (customer and process) in production planning. The topics are focusing mainly on intra-company planning tasks such as Master Planning, Demand Planning, Production Planning and Scheduling. Moreover, the associated hierarchical planning structure of such planning tasks has also been highlighted.

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The presented research is an effort to alleviate this problem by exploring whether built-in mobile sensors such as global positioning system (GPS), accelerometer, and gyroscope can be used as ubiquitous data collection and transmission nodes to extract activity durations for construction simulation input modeling. Collected sensory data are classified using machine learning algorithms for detecting various construction equipment actions. The ability of the designed methodology in correctly detecting and classifying equipment actions was validated using sensory data collected from a front-end loader. Ultimately, the developed algorithms can supplement conventional simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions.

In this work, we highlight the importance of production and sales plans for new products and illustrate the need for explicitly modeling supply uncertainties when making such decisions. We consider the case of variability in the production yield and perform extensive simulation experiments to study its impact on the performance of myopic and build-up policies in terms of the expected profit and risk measures. Managerial implications concerning selection of the production and sales plan are also discussed. The results show that ignoring the production yield variation can result in potentially incorrect decisions on the product launch time. The results also show that the policy selected based on the expected profit does not necessarily minimize risk.

This research proposes and tests an integrated framework for bottom-up simulation of performance in construction projects. The proposed framework conceptualizes construction projects as systems-of-systems in which the abstraction and micro-simulation of dynamic behaviors are investigated at the base-level consisting of the following elements: human agents, information, and resources. The application of the proposed framework is demonstrated in a numerical example related to a tunneling project. The findings highlight the capability of the proposed framework in providing an integrated approach for bottom-up simulation of performance in construction projects.

This paper presents a sensitivity analysis of unit capacity and patient flow for a hospital-wide system consisting of interdependent clinical and ancillary departments. The research employs system dynamics to model a hospital-wide system representative of a medium size, semi-urban, acute care community hospital. A sensitivity analysis using regression methods examines emergency department performance in the context of the hospital-wide system using a modified formulation of the Overall Equipment Effectiveness (OEE) hierarchy of metrics as a key performance indicator. The modified OEE metric demonstrates its usefulness first for the purpose of conducting a group screening design, and second for the purpose of performing the sensitivity analysis. The main results of the sensitivity analysis indicate that emergency department performance depends significantly on the unit capacity and patient flow in departments hospital-wide. The analysis provides quantitative insight into the important factors, the interactive relationships across departments, and evaluates the overall factor relative importance.

We consider the problem of identifying the solution to an optimization problem whose domain is a subset of the mixed-integer space and whose objective and constraint functions can only be observed via a stochastic simulation. In particular, we present cgR-SPLINE, a provably efficient algorithm on integer lattices. We additionally provide heuristics for algorithm parameter selection that have demonstrated good finite-time performance of cgR-SPLINE. Lastly, we present an extension of cgR-SPLINE for mixed-integer spaces and provide conjectures on the performance of the proposed algorithm.

When using simulations for decision making, no matter the domain, the uncertainty of the simulations' output is an important concern. This uncertainty is traditionally estimated by propagating input uncertainties forward through the simulation model. However, this approach requires extensive data collection before the output uncertainty can be estimated. In the worst case scenario, the output may even prove too uncertain to be usable, possibly requiring multiple revisions of the data collection step. To reduce this expensive process, we propose a method for inverse uncertainty propagation using Gaussian processes. For a given bound on the output uncertainty, we estimate the input uncertainties that minimize the cost of data collection and satisfy said bound. That way, uncertainty requirements for the simulation output can be used for demand driven data acquisition. We evaluate the efficiency and accuracy of our approach with several examples.

The generation of insight from simulation models has received little attention in the discrete-event simulation (DES) literature. Often DES studies claim to have supported problem understanding and problem solving by creating new and effective ideas, however little empirical evidence exists to support these statements. This paper presents an experimental study which aims to understand the role of simulation models in generating insights. Study participants are asked to solve a task based on a problem of a telephone service for non-emergency health care. One independent variable is manipulated: the features of the simulation model, forming three conditions. Participants either use the animation or only the statistical results of the model or no model at all to solve the task. A preliminary analysis of the pilot tests indicates that simulation models may assist users in gaining better understanding and in achieving divergent thinking.

In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing). In the proposed Iterative Simulation-Optimization (ISO) approach, we use a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities are used to introduce feedback constraints, that help exchange information between the two models. The proposed method is tested using benchmark instances from the OR library. The results indicate that the method gives near optimal schedules in a reasonable computational time.

The goal of this paper is to analyze drivers’ en-route divergence behaviors when a road way is blocked by a car incident. The Extended Belief-Desire-Intention (E-BDI) framework is adopted in this work to mimic real drivers’ uncertain en-route planning behaviors based on the drivers’ perceptions and experiences. The proposed approach is implemented in Java-based E-BDI modules and DynusT® traffic simulation software, where a traffic data of Phoenix in the U.S. is used to illustrate and demonstrate the proposed approach. For validation of the proposed approach, we compare the drivers’ en-route divergence patterns obtained by E-BDI en-route planning with the divergence patterns provided by Time Dependent Shortest Path (TDSP) finding algorithm of DynusT®. The results have revealed that the proposed approach allows us to better understand various divergence patterns of drivers so that a reliable traffic system considering impacts of the sudden road way blocking events can be designed.

This paper proposes a capacity reservation mechanism for a single-supplier and multi-manufacturer supply chain. The manufacturers first determine the production capacity they should reserve from the supplier, and then realize their reservations and place corresponding supplementary orders within a realization time window. The supplier builds its regular production capacity according to the reservations that have been received, and emergency production capacity for orders that exceed its regular capacity. Towards this end, we develop an analytical model to quantify the manufacturers’ optimal capacity reservation quantities and realization times, as well as the supplier’s optimal regular capacity. Given regular production capacity competition, a Cellular Automata (CA) simulation model is developed to resolve the analytical intractability of reservation realization time by modeling the manufacturers in an N-person game and identifying the convergence condition. Experiment results indicate that the proposed capacity reservation mechanism outperforms the traditional wholesale price contract in a decentralized supply chain.

This work examines how setting targets in organizations affects decision making. We assume a division acts to maximize the probability of meeting its given target. We use a simulation-based model to quantify the value gap that results from this target-based behavior in relation to utility maximizing behavior. We define an optimal target as one that minimizes the value gap. We investigate the effects of the organization’s risk aversion, the number of potential decision alternatives, and the distribution of the alternatives on both the value gap and the optimal target. The distribution of the alternatives is modeled with a copula based method. The results show that the optimal target (i) decreases as the risk aversion increases; (ii) increases as the number of available alternatives increase; and (iii) decreases as the alternatives approach some efficient frontier. We discuss the rationale and implications for the simulation results.

To provide automated access to multiple analysis tools, such as discrete event simulation or optimization, we extend current model-based systems engineering (MBSE) methodologies by introducing a new model to model transformation method based on object-oriented creational patterns from software design. Implemented in MATLAB’s discrete event simulation tool, SimEvents, we demonstrate the methodology by generating two distinct use cases based on a distribution supply chain and manufacturing system.

Simulation has been widely used in modeling engineering systems. A metamodel is a surrogate model used to approximate a computationally expensive simulation model. Extensive research has investigated the performance of different metamodeling techniques in terms of accuracy and/or robustness and concluded no model outperforms others across diverse problem structures. Motivated by this finding, this research proposes a bi-criteria (accuracy and robustness) optimized ensemble framework to optimally identify the contributions from each metamodel (Kriging, Support Vector Regression and Radial Basis Function), where uncertainties are modeled for evaluating robustness. Twenty-eight functions from the literature are tested. It is observed for most problems, a Pareto Frontier is obtained, while for some problems only a single point is obtained. Seven geometrical and statistical metrics are introduced to explore the relationships between the function properties and the ensemble models. It is concluded that the bi-criteria optimized ensembles render not only accurate but also robust metamodels.

The objective of this paper is to propose and test a framework for integrated assessment of infrastructure systems at the interface between the dynamic behaviors of assets, agencies, and users. For the purpose of this study a hybrid agent-based/mathematical simulation model is created and tested using a numerical example related to a roadway network. The simulation model is then used for investigating multiple performance scenarios pertaining to the road assets at the network level. The results include the simulation and visualization of the impacts of budget constraints on performance of the network over a forty-year policy horizon. Significantly the results highlight the importance of assessing the interactions between infrastructure assets, agencies, and users and demonstrate the capabilities of the proposed modeling framework in capturing the dynamic behaviors and uncertainties pertaining to civil infrastructure management.

We consider the problem of finding a zero of an unknown function, for which we are only provided with noise-corrupted observations. Stochastic Approximation (SA) is the most popular method for solving such problems, but SA requires the practitioner to selectively tune arbitrary parameters to generate convergence behaviour, where the tuning is very application specific. We propose a fully sequential Monte Carlo sampling method which replaces the hard-to-tune parameters with an adaptively sampled estimator whose quality is carefully assessed at each iteration of the stochastic recursion using a relative width confidence interval on its optimality gap. Under mild conditions, the asymptotic behavior of the proposed method is investigated for various adaptive sampling rules.

Answers to pressing questions in high-energy physics may lie in electroweak symmetry breaking, the phenomenon for explaining why the weak and electromagnetic forces are different. From solving the mystery of dark energy to string theory, our entire philosophy depends on the unknown physics at the electroweak scale. The hunt for the elusive Higgs boson has gone on for almost half a century. Its discovery was finally announced on July 4, 2012. The discovery of the Higgs boson is even more significant than often discussed. This grand experimental achievement in the largest, most powerful machine ever built, the Large Hadron Collider, marks a far wider scientific, philosophical and intellectual triumph – and one that spans human history from the dawn of civilization. It has to do with the idea of symmetry: amazingly, the Higgs boson was predicted to exist not for any physical reasons, but on strictly mathematical grounds based on arcane symmetries usually studied in "pure" mathematics. And the search for these symmetries involves a major quest that began with the Babylonians and Egyptians and continued to the ancient Greeks, the Arabs, medieval Europe, and on through the 19th century to our own time. This talk will begin with a brief overview of particle physics and why the Higgs Boson is so important and how it completes the symmetry. It will then explain how one goes about searching for this particle and expand the critical role simulation efforts played not only in the final analysis but also in designing the detector systems, which truly are “modern marvels.” I will discuss with some other highlights from these experiments and what to expect as the LHC gears up to come back on line in early 2015 at its design energy, nearly double its current 8 GeV operating point. Finally, I will close with a few words on particle physics and society and how the search for the perhaps esoteric has benefited society.

Agent-based modeling and simulation (ABMS) is a an approach to modeling systems comprised of autonomous, interacting agents. Computational advances are making it possible to develop agent-based models in a variety of application areas, including areas where simulation has not been extensively applied. Applications range from modeling agent behavior in supply chains, consumer goods markets, and financial markets, to predicting the spread of epidemics and understanding the factors responsible for the fall of ancient civilizations. Progress suggests that ABMS could have far-reaching effects on the way that businesses use computer models to support decision-making and how researchers use models as electronic laboratories. Some contend that ABMS “is a third way of doing science” and could augment traditional discovery methods for knowledge generation. This brief tutorial introduces agent-based modeling by describing key concepts of ABMS, discussing some illustrative applications, and addressing toolkits and methods for developing agent-based models.

Understanding the behavior of complex systems is becoming a crucial issue as systems grow in size, and the interconnection and geographical distribution of their components diversifies. The interaction over time of many components often leads to emergent behavior, which can be harmful to the system. Despite this, very few practical approaches for the identification of emergent behavior exist, and many are unfeasible to implement. Approaches using interaction as a measure of emergence have the potential to alleviate this problem. In this paper, we analyse absolute and relative methods that use interaction as a measure of emergence. Absolute methods compute a degree of interaction that characterizes a system state as being emergent. Relative methods compare interaction graphs of the system state with interaction graphs of systems that have been shown previously to exhibit emergence. We present these approaches and discuss their advantages and limitations using theoretical and experimental analysis.

To analyze the impact of intelligent traders with differing fundamental motivations on agent-based simulations of financial markets, we extend the classical zero-intelligence model of financial markets to a positive-intelligence model using the MASON agent-based modeling framework. We exploit multifractal detrended fluctuation analysis (MF-DFA) to analyze the series of stock prices generated by the positive-intelligence simulation. We study the changes in this output process as analyzed by MF-DFA when altering the mix of agents with competing market philosophies; and we compare and contrast the results of fitting conventional time series models to such output processes with the results of applying MF-DFA to the same processes.

Complex scheduling problems require a large amount of computation power and innovative solution methods. The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains. Independent specialized agents handle small tasks, to reach a superordinate target. Effective coordination is therefore required to achieve productive cooperation. Role models and distributed artificial intelligence are employed to tackle the resulting challenges. We simulate a NP-hard scheduling problem to demonstrate the validity of our approach. In addition to the general agent based framework we propose new simulation-based optimization heuristics to given scheduling problems. Two of the described optimization algorithms are implemented using agents. This paper highlights the advantages of the agent-based approach, like the reduction in layout complexity, improved control of complicated systems, and extendability.

Big data analytics is at the brink of changing the landscape in NXP Semiconductors Back End manufacturing operations. Numerous IT tools, implemented over the last decade, collect gigabytes of data daily, though the potential value of this data still remains to be explored. In this paper, the software tool called Heads Up is presented. Heads Up intelligently scans, filters, and explores the data with use of simulation. The software provides real-time relevant information, which is of high value in daily, as well as long term, production management. The software tool has been introduced at the NXP high volume manufacturing plant GuangDong China, where it is about to shift the paradigm on manufacturing operations.

This paper aims to shed some light on how the concept of cloud manufacturing has been applied to the semiconductor manufacturing operations. It starts with describing the challenges to the semiconductor manufacturing due to evolving of outsourcing business model in global context, then discusses the different forms of cloud manufacturing and proposes the semiconductor industry oriented architecture for cloud manufacturing. Serus is used as a case study to share how the cloud manufacturing has created the values for the customer and its outsourced suppliers in the semiconductor industry.

Diversified and complicated manufacturing sites make optimal scheduling of production lines difficult. Under current manufacturing processes, it is almost impossible for schedulers to consider all the constraints of production processes. A strategy of simulation-based APS is employed to overcome difficulties that interfere with satisfactory on-time delivery and commitment to the current status. In simulation-based scheduling, KPIs are important for selecting optimal dispatching rules in scheduling. In cases involving complex processes, in which the identification of appropriate KPIs is limited to selection among existing KPIs, KPIs should be chosen and modified carefully to optimize process management and to reflect all of the existing constraints of production. However, the existing methodologies for modifying KPIs are misplaced in complex manufacturing environments such as job-shop processes. We propose a method to design and select KPIs meeting the characteristics of given process, and verify empirically whether the KPIs meet requirements from experts of production lines.

A solution methodology and implementation components are presented that can uncover unwanted, unintentional or unanticipated effects on electric grids from changes to actual electric grid control software. A new design is presented to leapfrog over the limitations of current modeling and testing techniques for cyber technologies in electric grids. We design a fully virtualized approach in which actual, unmodified operational software under test is enabled to interact with simulated surrogates of electric grids. It enables the software to influence the (simulated) grid operation and vice versa in a controlled, high fidelity environment. Challenges in achieving such capability include achieving low-overhead time control mechanisms in hypervisor schedulers, network capture and time-stamping, translation of network packets emanating from grid software into discrete events of virtual grid models, translation back from virtual sensors/actuators into data packets to control software, and transplanting the entire system onto an accurately and efficiently maintained virtual-time plane.

Computer exploits often involve an attacker being able to compromise a sequence of hosts, creating a chain of "stepping stones" from his source to ultimate target. Stepping stones are usually necessary to access well-protected resources, and also serve to mask the attacker's location. This paper describes means of constructing models of networks and the access control mechanisms they employ to approach the problem of finding which stepping stone paths are easiest for an attacker to find, and the computational complexity of the problem as a function of knowledge we may have about limitations on attackers within a compromised host. While the simplest formulation of the problem can be addressed with deterministic shortest-path algorithms, we argue that consideration of what and how an attacker may (or may not) launch from a compromised host pushes one towards simulation based solutions.

The ubiquity of smart phones and mobile devices has led to new security challenges in the form of mobile malware. Understanding its spread requires a high performance computing approach that involves: (i) a realistic and detailed representation of mobile social networks in urban environments, and (ii) a scalable simulation environment for malware diffusion.

We use Epicure (Channakeshava et al., IPDPS 2011), an individual based HPC simulation tool for malware modeling, that scales to networks with over 10M individuals. We compare malware dynamics in realistic networks with random mobility models and study the impact of key properties associated with the worm and mobility models. We use detailed statistical analysis to identify the significant parameters and their interaction. Finally, we use Epicure to study SMS/MMS based malware and hybrid malware that use both proximity based Bluetooth networks and infrastructure based cellular network to replicate on other susceptible devices.

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

Net-zero buildings emphasize balance between the consumption and production of resources, resulting in structures that are not only more efficient, but potentially restorative. While historically applied to energy use, the net-zero framework is also relevant to water management. Both the building energy and water sectors consist of demand loads that must be met by available sources. Variations in load design, source allocation, and human interaction result in numerous arrangements that require evaluation to meet efficiency goals. Decision support systems aimed at building energy are abundant, whereas building water tools are limited. The dynamic nature of the building water cycle necessitates flexible modeling tools that can predict and assess future water consumption and production trends at varying resolutions and under fluctuating conditions. This paper presents opportunities for simulation modeling to support net-zero water achievement and introduces an integrated building water management (IBWM) model for on-site water balance decision support.

A case of study applied to the sports context (Gremio Arena Soccer in Brazil), this paper has the objective to measure dynamically the time spent during the accommodation of fans in a stadium bleacher. The metric used to evaluate it was the occupation time. For this analysis, it was considered four different occupation scenarios of the bleachers characterizing ways to drive fans to their seats. The selected scenarios were: (1) random entry, (2) entry driven by a dispatchers, (3) numbered seats; (4) numbered seats with dispatcher. To built the model it was used the software SIMIO, capable to tackle this type of experiment. With a conclusive recommendation it was observed that the policy based on the dispatcher results in a shorter total occupation time. Additionally, it was analyzed factors that should make difference, such as physical feasibility to support the large number of people, as well as cultural aspects.

Real Time Strategy (RTS) games provide complex domain to test the latest artificial intelligence (AI) research. In much of the literature, AI systems have been limited to playing one game. Although, this specialization has resulted in stronger AI gaming systems it does not address the key concerns of AI research, which focuses on the development of AI agents that can autonomously interpret, learn, and apply new knowledge. To achieve human level performance, current AI systems rely on game specific knowledge of an expert. This paper proposes a RTS language in hopes of shifting the current research focus to the development of general RTS agents. General RTS agents are AI gaming systems that can play any RTS game, defined in the RTS language. The structure of the RTS language prevents game specific knowledge from being hard coded into the system, thereby facilitating research that addresses the fundamental concerns of artificial intelligence.

Gaming simulation allows for experiments with sociotechnical systems and has as such been employed in the railway sector to study the effects of innovations on robustness and punctuality. Systems work as non-trivial machines and the effect of an innovation on a dependent variable is potentially context, time and history dependent. However, several constraints inhibit the use of validity increasing measures such as repeated runs and increasing sample size. Based on a debriefing framework, insights from qualitative process research and six games with Dutch and UK railway traffic operators, we provide a guide on how to assess and increase reliability and validity. The key is for game players, observers and facilitators to open up the black box and thereby assessing how the innovation brought about any changes, if these changes are insensitive to changes in parameters and if the conclusions hold outside the game.

Management games have a long history in management and social science education, and a large number of such games has been developed and is being used in university education and in professional training. With the increasing use of computers in recent decades, most of them have been developed in computerized form. However, typically, these games are being developed in isolation, without using any general model, or methodology, or simulation engineering framework. In this paper we propose a basic conceptual model for business management games based on the classical Lemonade Stand Game and we show how to construct incremental extensions of this model and how to implement them as web-based simulations using standard web technologies.

This case study appraises the manufacturing operations of 3M’s High Capacity Conductor product (Power Line Transmission Cable). For this product, achieving specified continuous product lengths, not overall throughput, is the measure of success. Target length is only achieved when the process runs without a failure and without interruption of a key material input that is also manufactured to length but on a separate production line. What are the optimum length targets for both processes when “almost” isn’t good enough? Could integrating these processes be a valuable shift? We know that modeling provides value added decision support for complex problems, but can a problem be too complex to model? How do we, as simulation practitioners, navigate real world complexity and find our way to model simplicity that provides meaningful direction and solid return on invested time? These questions and more are presented and discussed in this manufacturing case study.

Product handling of fragile packages requires specific attention to physical interactions. Factors such as product inertia, conveyor-load friction, and collision velocities all contribute to the final quality of the product. At times, collisions between fragile products are unavoidable. With a focus on maintaining both product flow and unit quality, manufacturing simulations demand specific attention to kinetic interactions. This requires a simulation environment capable of quantifying the impact of these interactions. Controls techniques are tested within these simulated environments for future use in process control.

This case study describes the use of a discrete event simulation model to create a pseudo-schedule for a high-mix, low-volume printed circuit board shop, which is managed using only queue and dispatch heuristics. By connecting a simulation model that emulates those heuristics to current state data extracted from the shop’s ERP system yields a forecast or schedule of predicted events that the shop supervisor is able to use to manage human and machine resource allocation and see the impact of his decisions on predicted order completion dates. The scheduling system is driven by an Access database that collects system configuration and state data each day. An Arena simulation model then starts its run by configuring the model in accordance with the database, using the Resource and Station Sets features. The model runs, writing out event messages that the database then collates into a finite-capacity forward-planned schedule for the shop.

An improved variance reduction method for accurate estimation of the price, delta, and gamma of Asian options in a single simulation is presented. It combines randomized quasi-Monte Carlo with very efficient new control variates, that are especially successful in reducing the variance of the pathwise derivative method used to simulate delta and gamma. To improve the performance of randomized quasi-Monte Carlo, we smooth the integrands by employing conditional Monte Carlo and reduce the effective dimension of the smoothed integrands by using principal component analysis. Numerical results show that the new method yields significant variance reduction for the price, for delta and for gamma.

Drawing upon the work of Ghamami and Zhang [2013], this paper presents an efficient Monte Carlo framework for the estimation of credit value adjustment (CVA), one of the most widely used and regulatory-driven counterparty credit risk measures. Our proposed efficient CVA estimators are developed based on novel applications of well-known mean square error (MSE) reduction techniques in the simulation literature. Our numerical examples illustrate that the efficient estimators outperform the existing crude estimators of CVA substantially in terms of MSE.

The square-root process is used to model interest rates and volatility in financial mathematics. The pricing of derivatives involving that process often requires simulating it, since there are often no explicit formulas for prices. We study how a change of measure (CM) may improve those simulations. We compare with Andersen’s quadratic-exponential scheme (QE), which so far appears to be the most efficient technique to simulation the stochastic differential equation satisfied by the square-root process. An integer-dimension squared Bessel process, easy to simulate, is used to generate the law of the square-root process using a change of measure. The new method performs very well, and the two algorithms execute at similar speeds; however, CM is slower than QE if random number generation is taken into account, because CM requires more random numbers. The Radon-Nikodym derivative sometimes has a rather intriguing behavior, which is itself of interest. We propose an explanation.

Agent-based modeling (ABM) has gained great popularity in recent years, especially in application areas where human behavior is important, because it opens up the possibility of capturing such behavior in great detail. Hybrid models which combine ABM with discrete-event simulation (DES) are particularly appealing in service industry applications. However in this paper we argue that many of the so-called distinctions between agents in an ABM and entities in a DES are artificial, and we describe several DES models which use standard entities to represent agent-like behaviors.

Simulation models have been used extensively to evaluate and aid in planning of health screening strategies in health care. In typical simulation models, patient screening behavior is often assumed as homogeneous (patient characteristics do not influence the probability of undergoing screening) and rigid (invariant with time, and not malleable by screening interventions), and modeled as such. However, patient screening behavior in reality is heterogeneous and malleable. Disregarding these realities affect the model functionality and modeling outcomes. We propose two general simulation model structures: one representing typical existing simulation model and another representing simulation model that incorporates heterogeneity and malleability of patient screening behavior. We then illustrate both model structures by implementing it in the case of Diabetic Retinopathy eye screening. Comparison of the two resulting models indicate that heterogeneity and malleability of patient screening behavior should be addressed in simulation models to improve model functionality and precision of outcome estimates.

This paper discusses the development of a simulation model to mimic a return to work phenomenon of Social Security Disability Insurance (SSDI) enrollees in the United States. Agent Based and Bayesian Network methods are used within a multi-method simulation model to capture system conditions and enrollee behavior. Bayesian Network is used within an agent to represent enrollee’s decision to work. A developed simulation model can be used to investigate many aspects of the return to work phenomenon. The model is used to answer a sample research query that examines how the perception of an enrollee on work incentives related to health improvements, money, and vocational assistance can affect the return to work phenomenon for 18 to 39 year old SSDI enrollees (at enrollment). This is measured as the total percentage of population with benefits terminated for work.

When estimating a performance measure f_* of a complex system from noisy data over a domain of interest, the underlying function f_* is often known to be convex. In this case, one often uses convexity to better estimate f_* by fitting a convex function to data. The traditional way of fitting a convex function to data, which is done by computing a convex function minimizing the sum of squares, takes too long to compute. It also runs into an "out of memory" issue for large-scale datasets. In this paper, we propose a computationally efficient way of fitting a convex function by computing the best fit minimizing the sum of absolute deviations. The proposed least absolute deviations estimator can be computed more efficiently via a linear program than the traditional least squares estimator. We illustrate the efficiency of the proposed estimator through several examples.

The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this work is to see whether it is worth including the gradients versus their computation time. Test results of two analytical functions and a fluid-structure interaction (FSI) problem from bio-mechanics show that this approach, known as Gradient Enhanced Kriging (GEK), can significantly enhance the accuracy of Kriging models even when the gradient data is only partially available.

An important step in input modeling is the assessment of data being independently and identically distributed (IID). While this is straightforward when modeling stationary stochastic processes, it becomes more challenging when the stochastic process follows a non-stationary pattern where the probability distribution or its parameters depend on time. In this paper, we first discuss the challenges faced by using traditional approaches. We then introduce the Histograms and Rates for Input Analysis (HistoRIA) as a tool to facilitate input modeling. The tool automates the analysis process and significantly reduces the amount of time and effort required to test the IID assumptions. The generated HistoRIA plot is capable of effectively illustrating changes in the rate and distribution over time. Although originally designed and developed for simulation input analysis, the paper demonstrates how the tool can potentially be applicable in other areas where non-stationarity in the data is also common.

A three-level, hierarchical system for yard crane (YC) management in container terminals and the algorithms for the bottom two levels were proposed in previous studies. The bottom two levels are responsible to YC job sequencing and intra-row YC deployment. This paper presents YC deployment strategies for inter-row YC deployment. The objectives are to minimize vehicle waiting times and the number of overflow jobs. We show by realistic simulation experiments that (1) when the number of yard cranes is less than the number of yard blocks, deploying YCs in proportion to the number of jobs in each row (3L-Pro-Jobs) is the best; (2) when the number of yard cranes is equal to or more than the number of yard blocks, the apparent workload approach, 3L-AW, performs best.

Yard crane (YC) dispatching in the operational planning of container terminals usually aims to minimize makespan of YC operations or waiting time of vehicles. We propose that minimizing the maximum tardiness of vehicle jobs at yard blocks will minimize the operational delay of the longest quay crane (QC). This will minimize vessel turnaround time which is one of the most important objectives of container terminals. A provably optimal algorithm, MMT-RBA* to minimize maximum job tardiness, is presented to sequence the YC jobs. Jobs requiring reshuffling of other containers, often ignored in other studies, are handled by embedded simulation in our optimization algorithms. Another provably optimal algorithm, MMS-RBA* to minimize makespan, is also presented. Simulation experiments confirm that MMT-RBA* significantly outperforms the optimal algorithm RBA* to minimize vehicle waiting time and MMS-RBA* to minimize makespan in minimizing vessel turnaround time.

To provide better services for shipping companies and to increase profits, improving the operation equipment efficiency and reducing the ship dwelling time at the terminal is an important problem for port companies. Port management information system and information technology is widely used for supporting and controlling the terminal operation, which can track the operation data simultaneously. In this study, a simulation model is constructed using historical Global Positioning System tracking data and analyzed for application to the shipping industry. For improving handling equipment efficiency, flexibility analysis is performed for the trailers that served gantry cranes for ship operation (container loading and unloading processes) by comparing the different dispatching scenarios after performing the simulation. The proposed procedure to construct simulation models of container terminal was found to be both practical and powerful.

This panel will discuss the state of the art in simulation optimization research and practice The participants include representation from both academia and industry: Michael Fu, chair (Maryland), Ilya Ryzhov, co-chair (Maryland), Güzin Bayraksan (Ohio State), Shane Henderson (Cornell), Barry Nelson (Northwestern), Warren Powell (Princeton), Ben Thengvall (OptTek Systems). The industry participant represents one of the leading software providers of optimization tools for simulation. An update of the online testbed of simulation optimization problems for the research community (http://www.simopt.org) will be provided.

In this paper, we consider the modeling and simulation of low-volume mixed model assembly lines that can be found in the aerospace industry. Low-volume mixed model assembly processes are characterized by a large amount of tasks to be manually performed, buffer space constraints, specialized resources like jigs and tools, and a large number of external suppliers. The main principles of modeling and simulating such manufacturing systems are discussed. Based on a domain analysis, the major building blocks of simulation models for low-volume mixed model assembly lines are derived. We exemplify their implementation using the commercial discrete-event simulation tool AutoSched AP. As an application of these building blocks, we analyze the cabin installation process in a final assembly line in aircraft production using discrete-event simulation.

A U-shaped production line is considered one of the most flexible designs used by companies to adapt to varying production conditions and to implement lean concepts. Similarly, work-sharing allows for cross-training of a flexible workforce while achieving high levels of worker utilization. This paper proposes a new protocol for U-shaped assembly lines that relies on work-sharing principles and on an adaptation of bucket brigades to cellular environments. Discrete event simulation is used to maximize throughput while determining buffer locations and buffer levels for each worker. This model is validated with a physical simulation and then tested with industry data. The results show the protocol enables a high level of throughput and worker utilization for the manufacturing cell while capping the maximum amount of WIP in the system. The proposed protocol is generalizable with respect to the number of stations, processing times, types of processes, and worker velocities.

We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In this paper, we capture the dependence between input variables in an undirected graphical model and decouple the statistical estimation of the univariate input distributions and the underlying dependence measure into separate problems. The estimation errors due to finiteness of the real-world data introduce the so-called input uncertainty in the simulation output. We propose a method that accounts for input uncertainty by sampling the univariate empirical distribution functions via bootstrapping and by maintaining a posterior distribution of the precision matrix that corresponds to the dependence structure of the graphical model. The method improves the coverages of the confidence intervals for the expected profit the per period.

In this paper we describe a highly scalable multi-modal traffic simulation platform on parallel and distributed environments ranging from multi-core or many core machines to multi-node clusters or supercomputer environments. We evaluated our platform with multi-modal transportation networks from the capital city of Ireland, Dublin, and verified its high scalability and real-time performance on both a 1-node with 12 core machine for a multi-threading environment and a cluster of 12 nodes (with a total of 144 cores) for a distributed system. With this platform, municipal planners or traffic engineers at transportation companies can quickly conduct many series of simulations with various what-if scenarios in a parallel and distributed manner, allowing them to assess and select the best responses to sudden incidents, or plan for major events or disaster responses.

Many social simulations can be represented using mobile-agent-based model in which agents moving around on a given space such as evacuations, traffic flow and epidemics. Whole planet simulation with billions of agents at microscopic level helps mitigate the global crisis. It introduces new technical challenges such as processing and migrating many agents and load balancing among hundreds of machines. To overcome these challenges, well-designed software architecture of a simulator is essential. In this research, we proposed agent-based complex cellular automata architecture (ABCCA) and studied the performance and scalability of two cell-based processing models, through simple traffic flow simulation on multi-core distributed system. The experiments show that the computation speedup can be achieved by reducing granularity of tasks and processing only active spaces. We achieved running the traffic flow simulation with one billion of agents in almost real time on 1,536 CPU cores of total 128 machines of TSUBAME supercomputer.

This paper proposes using intermediate features of traffic simulations in a genetic algorithm designed to find the best scenarios in regulating traffic with multiple objectives. A challenge in genetic algorithms for multi-objective optimization is how to find various optimal scenarios within a limited decision time. Typical evolutionary algorithms usually maintain a population of diversified scenarios whose diversity is measured only by the final objectives available at the end of their simulations. We propose measuring the diversity by also the time series of the objectives during the simulations. The intuition is that simulation scenarios with similar final objective values may contain different series of discrete events that, when combined, can result in better scenarios. We provide empirical evidence by experimenting with agent-based traffic simulations showing the superiority of the proposed genetic algorithm over standard approaches in approximating Pareto fronts.

The block time (BT) schedule, which allocates Operating Rooms (ORs) to surgical specialties, causes inflexibility for scheduling outside the BT, which negatively affects new surgeons, new specialties, and specialties that have fluctuation in the number of surgeries. For this inflexibility, we introduce the concept of releasing ORs, and present a generic simulation and evaluation framework that can be used by hospitals to evaluate various release mechanisms. The simulation and evaluation framework is illustrated by a case study at Vanderbilt Medical Center and University (VUMC) in Nashville. The results show that introducing a release policy has benefits in decreasing the number of unscheduled patients and decreasing access time, without affecting the specialties originally assigned to the released rooms.

Before the day of surgery, it is common for hospitals to take advantage of block release time in order to better fill operating rooms (ORs) and increase room utilization levels. Surgery groups are forced to release unscheduled OR time, which then becomes available for other groups to use. In this paper, we investigate release policies based on various surgery arrival distributions, capacity levels, and case durations. We show the tradeoffs of different policies involving assigned block and open posting rooms’ utilization levels and number of cases not accommodated in the schedule. Our results show that block release has a minor benefit for services with high room utilization (at or above 80%). Services with lower room utilizations may benefit from release, but one must consider whether to use block release or to reallocate the service’s block time.

Perioperative services have a high impact on a hospital’s financial success. In order to increase patients’ privacy and satisfaction, while restraining cost, a redesign of the existing Post-Anesthesia Care Unit (PACU) was suggested at the Mayo Clinic. A simulation model was created to determine the number of beds required in the redesign of the PACU to maintain Operating Room (OR) blocking below 5 %. Since OR time and resources are more costly than the PACU, limiting the resource scarcity of the PACU should minimize delays through the surgical suites. By assuming PACU resourcing as secondary to managing the OR, the underlying complexity of the surgical scheduling did not have to be analyzed. Real data was fed into the simulation model that successfully captured the complexity of the system without the work-intensive requirements of theoretical modeling. The results of the analysis were incorporated into the design plans for remodeling the PACU.

A demonstration of GIS and agent-based capabilities in the new AnyLogic 7.1. See how AnyLogic completely redesigned GIS, ensuring ease of model building & run time, and the ability to integrate agents and GIS maps. The demonstration will feature additional GIS improvements including referencing searched locations, the availability of pan and zoom, and obtaining routes dynamically. Watch the model being built and run in this deep dive into GIS and agent-based modeling with AnyLogic.

This paper describes the Simio modeling system that is designed to simplify model building by promoting a modeling paradigm shift from the process orientation to an object orientation. Simio is a simulation modeling framework based on intelligent objects. The intelligent objects are built by modelers and then may be reused in multiple modeling projects. Although the Simio framework is focused on object-based modeling, it also supports a seamless use of multiple modeling paradigms including event, process, object, systems dynamics, agent-based modeling, and Risk-based Planning and Scheduling (RPS).

Modern facilities, such as automated material handling systems, baggage handling, or manufacturing process systems, are complex systems controlled by various control units on different automation levels. The design and development of these facilities require the application of CAD, simulation, development and testing tools. Traditionally different tools have been used at each stage, and that separation has tended to impede the smooth delivery of the project. In this paper, we show that the same modeling software can be deployed at all stages of the design – from layout and simulation through to controls testing. Additionally we show that by incorporating a PLC emulator, tightly integrated with the virtual clock, and using internal communication channels, that real control logic can also be used at every stage. The engineer has complete control over the level of accuracy for modeling, simulating and emulating, while still running the model faster or slower than real-time.

See examples of online predictive analytics simulations and learn how to get your simulation running on the web in a free Forio Epicenter account. If you don’t have a model with you, you can use a sample model to produce an interactive web simulation. Epicenter supports simulations developed in R, Python, Julia, Vensim, Excel, and other languages.

We will start with an introduction to the Forio platform. In the first part we will help you get your model on Forio’s servers. We’ll walk through the process of importing your model on the server. In the second half we’ll focus on creating an interactive user interface for your application. After the introduction, you will be able to work on your own. Forio will also provide a debrief on online simulations and suggest possible next steps for enhancing your own online tool.

In this paper verification and validation of simulation models are discussed. Different approaches to deciding model validity are described and a graphical paradigm that relates verification and validation to the model development process is presented and explained. Conceptual model validity, model verification, operational validity, and data validity are discussed and a recommended procedure for model validation is presented.

This talk will begin with an overview of the life of John Swanson, showing that his success was due to a community effort, and celebrating the people and institutions which made his engineering contributions possible. A milestone in this path is the awarding of the John Fritz Medal, said to be “the highest award in the engineering profession”. The rest of this talk will be a non-technical history of ANSYS, the world’s premier engineering simulation software. Discussion will include the technical, engineering, and business factors and decisions which put ANSYS on the path to global success. Questions will be welcome at the end of the talk, and at the conference after the luncheon address.

Commercial buildings consume nearly 20% of all energy used in the USA, costing over $200 billion each year. Building envelopes plays a key role in determining how much energy is required for the operation of a building. Thermal and solar properties of glazing and shading systems only provide information based on static evaluations, but it is critical to assess the efficiency of these systems as a whole assembly under site specific conditions. With an ever increasing cooling energy demand of buildings in hot and humid climates like in Florida, using a well-designed window-shading system is considered an efficient strategy that minimizes direct sunlight reaching indoors and thus reduces the overall energy loads. While energy loads reduction is important, the indoor comfort of occupants cannot be compromised. This research was conducted to analyze the indoor thermal and visual performance of various window-shading assemblies that were selected after their energy performance evaluation.

Adjusting for occupancy, when controlling an HVAC (Heating, Ventilation, and Air Conditioning) system, is an important way to realize demand-driven control and improve energy efficiency in buildings. Energy simulation is an efficient way to examine the effects of occupancy on a building’s energy consumption and a cost-effective and non-intrusive solution to test occupancy-based HVAC control strategies. However, there are more than one hundred building energy simulation programs used in research and practice, and large discrepancies exist in simulated results when different simulation programs are used to model the same building under same conditions. This paper evaluates different methods and sequences of coupling occupancy information with building HVAC energy simulation. A systematic review is conducted to analyze five energy simulation programs, including DOE-2, EnergyPlus, IES-VE, ESP-r, and TRNSYS, from the following five perspectives of heat transfer and balance, load calculation, occupancy-HVAC system connection, HVAC system modeling, and HVAC system simulation process.

General purpose graphics processing units (GPGPUs) suitable for general purpose programming have become sufficiently affordable in the last three years to be used in personal workstations. In this paper we assess the usefulness of such hardware in the statistical analysis of simulation input and output data. In particular we consider the fitting of complex parametric statistical metamodels to large data samples where optimization of a statistical function of the data is needed and investigate whether use of a GPGPU in such a problem would be worthwhile. We give an example, involving loss-given-default data obtained in a real credit risk study, where use of Nelder-Mead optimization can be efficiently implemented using parallel processing methods. Our results show that significant improvements in computational speed of well over an order of magnitude are possible. With increasing interest in "big data" samples the use of GPGPUs is therefore likely to become very important.

In literature, multi-objective particle swarm optimization (PSO) algorithms are shown to have potential in solving simulation optimization with real number decision variables and objectives. This paper develops a multi-objective PSO algorithm based on weighted scalarization (MPSOws) in which objectives are scalarized by different sets of weights at individual particles while evaluation results are shared among the swarm. Various scalarizing functions, such as simple weighted aggregation (SWA), weighted compromise programming (WCP), and penalized boundary intersection (PBI) can be applied in the algorithm; and to better achieve diversity and uniformity preservation of the Pareto set, a hybrid external archiving technique is proposed consisting of both KNN and ϵ-dominance methods. Numerical experiments on noise-free problems are conducted to show that MPSOws outperforms the benchmark algorithm and WCP is the most preferable strategy for the external archiving. In addition, simulation allocation rules (SARs) can be further applied with MPSOws when evaluation error is considered.

This paper is concerned with continuous simulation optimization problems with stochastic constraints. Thus both the objective function and constraints need to be estimated via simulation. We propose an Adaptive Search with Discarding and Penalization (ASDP) method for solving this problem. ASDP utilizes the penalty function approach from deterministic optimization to convert the original problem into a series of simulation optimization problems without stochastic constraints. We present conditions under which the ASDP algorithm converges almost surely, and conduct numerical studies aimed at assessing its efficiency.

Out-of-shelf events refer to periods of time in which items of a certain product are not available to customers. It is clear that incidents of this nature result in economic loss, but their side effects are much more profound: since there is no record of missed sales opportunities, the estimated demand curve tends to be inaccurate. As a result, order placement strategies employed by retailers are based on imprecise forecast models, so further out-of-shelf events are very likely to occur: a vicious cycle, hence, arises. In this work, we propose a multi-agent-based simulation to evaluate the impact of out-of-shelf events that considers the reactions of customers towards these incidents and retailers’ ordering strategies. Our results show that these events have a significant effect on demand estimation and that multi-agent-based simulations may provide interesting insights and support for the development of more accurate forecast models in retail.

The persistent crisis in Syria has affected millions of its citizens by forcing their displacement from native or accustomed residences. Modeling the Syrian conflict provides a computational means to better understand why, when, and where these citizens flee. Thus, an agent-based model drawn on real-world data to represent Syrian cities (the environment) and the demographic constitution of those cities (the agents) has been developed and is explained in this discussion. The outputs of the model accurately reflect population displacement as it occurred between the years 2011-2012. Importantly, the purpose of this agent-based modeling and the output analysis is to develop a means to anticipate, measure, and assess future displacement in Syria as well as to model other threatened populations in crises where displacement might occur. This paper presents the methodology to crafting the environment and agents to represent the Syrian city of Aleppo and the displacement of its citizens.

Revenue management theory and practice frequently rely on simulation modeling. Simulations are employed to evaluate new methods and algorithms, to support decisions under uncertainty and complexity, and to train revenue management analysts. For all purposes, simulations have to be validated. To enable this, they are calibrated: model parameters are adjusted to create empirically valid results. This paper presents two novel approaches, in which genetic algorithms contribute to calibrating revenue management simulations. The algorithms emulate analyst influences and iteratively adjust demand parameters. In the first case, genetic algorithms directly model analysts, setting influences and learning from the resulting performance. In the second case, a genetic algorithm adjusts demand input parameters, aiming for the best fit between emergent simulation results and empirical revenue management indicators. We present promising numerical results for both approaches. In discussing these results, we also take a broader view on calibrating agent-based simulations.

The Poisson process has been an integral part of many models for the arrival process to a telephone call centers. However, several publications in recent years suggest the presence of a significant "overdisperson'' relative to the Poisson process in real-life call center arrival data. In this paper, we study the overdispersion in the context of ``heavy traffic'' and identify a critical factor that characterizes the stochastic variability of the arrivals to their averages. We refer to such a factor as the scaling parameter and it potentially has a profound impact on the design of staffing rules. We propose an new model to capture the scaling parameter in this paper.

Even when they are known to be continuous, Poisson-process rate functions are sometimes specified as piecewise constant. To better approximate the unknown continuous rate function, we fit a piecewise-quadratic function. In addition to maintaining the rate's integral over each time interval, at each interval's end point we match the rates and their first derivatives. For every interval with negative rates, we force non-negativity by taking the maximum of zero and the quadratic-function value, modifying the quadratic to keep the original integral value. These rate functions can be used alone or applied after one or more iterations of I-SMOOTH, an existing algorithm (by the first two authors) designed for the same problem. We briefly discuss random-process generation from the new rate functions. Finally, we provide examples.

We consider the problem of using observations of a counting process measured multiple times over a period to create an intensity function for a Non-Homogeneous Poisson Process (NHPP) that estimates the observed process, is consistent with the data, is continuous, and is "smooth". Optionally, we may also require the intensity value at the end of the period be identical to the intensity value at the beginning of the period, for application in contexts where the period of interest is inherently cyclic, e.g., a day, or a week.

Our approach is to define a class of continuous piecewise linear intensity functions and formulate the problem as a constrained quadratic programming problem whose solution is obtained through the solution of a simultaneous set of linear equations. We describe the approach, identify conditions under which feasible solutions are assured to exist, and study the behavior of the solutions on some example problems.

Experiences in the military M&S domain show that verification and validation (V&V) is still often more of an afterthought. This is due to that V&V is considered by many as a difficult, costly and intangible practice, which highly depends on the M&S context. Due to decreasing budgets the Dutch MoD is increasing its reliance on M&S to ensure their operational effectiveness. Given this increased reliance the Dutch MoD expressed the need for a V&V standard and a permanent V&V service provision organization. The Dutch National Aerospace Laboratory NLR and the Netherlands Organization for Applied Scientific Research TNO were tasked to realize this objective by establishing a V&V expertise center, named Q-tility. This presentation shows, by means of actual V&V examples, the Q-tility V&V life-cycle model activities and underlying methods, tools and techniques, along with how it protected and created added value for the involved M&S developer and user organizations.

The U.S. Department of Defense uses large-scale simulation to analyze how budgeted capabilities and capacities map to risk in various scenarios. A model called Synthetic Theater Operations Research Model (STORM) is used to assess risk in an integrated, campaign setting. Ultimately, analyses performed with STORM inform the decisions made by the Services for future resources. STORM—a large, stochastic campaign-level simulation that models hundreds of entities with tens-of-thousands of interactions over a multi-week campaign—requires many inputs and generates gigabytes of output data. These enormous data sets need to be turned into an analysis product. We are developing tools and methods that reduce the amount of manpower and time required to complete STORM output post-processing; determine a sufficient number of replications to perform; support STORM scenario quality validation; and boost the speed and precision with which analysts are able to gather insights from a set of simulation runs.

In semiconductor manufacturing each silicon wafer is processed through hundreds of sequential steps. At each step manufacturing tools use a specific recipe to process the silicon wafer. Each group of tools has to be able to run dozens if not hundreds of unique recipes needed to process the wafers at all steps for several technologies. The challenge is to determine how many tools within each tool group need to be qualified to run each recipe in order to provide enough processing lanes for product to flow through the toolset so as to not adversely affect cycle time. Queuing theory equations have been successfully used to estimate cycle time using historical factory variability data. This study examines the use of these equations to estimate recipe specific tool counts given the expected utilization driven by each recipe spec and a desired cycle time target.

Data mining tools have been around for several decades, but the term "big data" has only recently captured widespread attention. Numerous success stories have been promulgated, as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to this big data paradigm is that it relies on observational data - limiting the types of insights that can be gained. The simulation world is different. A "data farming" metaphor captures the notion of purposeful data generation from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships. With this new mindset, we can achieve quantum leaps in the breadth, depth, and timeliness of the insights yielded by simulation models.

Stochastic composite simulation models, such as those created via the IBM Splash prototype platform, can be used to estimate performance measures for complex stochastic systems of systems. When, as in Splash, a composite model is made up of loosely coupled component models, we propose a method for improving the efficiency of composite-model simulations. To run N Monte Carlo replications of the composite model, we execute certain component models fewer than N times, caching and re-using results as needed. The number of component-model replications is chosen to maximize an asymptotic efficiency measure that balances computation costs and estimator precision. In this paper we initiate the study of result-caching schemes by giving an exact theoretical analysis for the most basic two-model scenario, as well as outlining some approaches for obtaining the parameter values needed for result caching.

Public health agencies traditionally rely heavily on epidemiological reporting for notifiable disease control, but increasingly apply simulation models for forecasting and to understand intervention tradeoffs. Unfortunately, such models traditionally lack capacity to easily incorporate information from epidemiological data feeds. Here, we introduce particle filtering and demonstrate how this approach can be used to readily incorporate recurrently available new data so as to robustly tolerate – and correct for – both model limitations and noisy data, and to aid in parameter estimation, while imposing far less onerous assumptions regarding the mathematical framework and epidemiological and measurement processes than other proposed solutions. By comparing against synthetic ground truth produced by an agent-based model, we demonstrate the benefits conferred by particle filtering parameters and state variables even in the context of an aggregate, incomplete and systematically biased compartmental model, and note important avenues for future work to make such approaches more widely accessible.

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and their management. The implementations of these generic ideas in AutoMod, SLX, ExtendSim, and Simio are described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including discussion of AutoMod, SLX, ExtendSim, Simio, Arena, ProModel, and GPSS/H.

Changeover setups are induced by switching manufacturing processes among products. They commonly exist in flexible manufacturing systems. Modeling their queue time impact correctly is of fundamental importance in evaluating the performance of production systems. In this paper, the mean queue time approximation models are proposed based on the properties of changeover setups. The models are validated by simulations and perform well in the examined cases.

This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

Measuring Cycle Time gains was not consistent in Semiconductor manufacturing Fab. A method was required to standardize how to measure Cycle Time, and that could measure Cycle Time irrespective of the nodes being built at manufacturing Fab. Once Cycle Time can be measured consistently from manufacturing Fab, project could be developed to reduce Fab Cycle Time and measured to verify results at the completion of the project. This paper will show the steps taken to develop a standardized method of measuring Cycle Time through the use of the Queuing Theory formula (G/G/m), where the Queuing Theory Formula Parameter data was collected from, how the Cycle Time data was validated for accuracy, how the Cycle Time data was used to identify areas of improvement, and how the completed Cycle Time project data was feed back into the Queuing Theory formula for final validation.

Index terms: Queuing Theory, Cycle Time, Queuing Parameters

The idea of the magic circle, frequently referenced in the serious gaming community, is explained and then shown to be a possible perspective to view live-virtual-constructive (LVC) simulation federations. This view opens the method of categorizing different roles and data capabilities for the various elements and actors within an LVC federation. The applicability of this view is shown in some explanatory detail, including a description of its applicability to simulations, the different types of roles, and the variety of knowledge (procedural and propositional) that can be qualified when the federation is viewed this way. Structured identification of the federation elements and the data they are exchanging is relied on to describe peculiar data interoperability issues that exist within current LVC federation architectures, and which may also affect future LVC federation architectures currently under development.

A Distributed Virtual Environment (DVE) system provides a shared virtual environment where physically separated users can interact and collaborate over a computer network. There are three major challenges to improve DVE scalability: effective DVE system performance measurement, understanding the controlling factors of system performance/quality and determining the consequences of DVE system changes. We describe a DVE Scalability Engineering (DSE) process that addresses these three major challenges for DVE design. The DSE process allows us to identify, evaluate, and leverage trade-offs among DVE resources, the DVE software, and the virtual environment. We integrate our load simulation and modeling method into a single process to explore the effects of changes in DVE resources.

This paper discusses the implementation of the Trading Competition held at the 2014 IEEE Computational Intelligence in Financial Engineering conference (CIFEr 2014). Participants in the competition were asked to hedge a simulated portfolio of assets, worth approximately 54 million. The winner was the individual whose portfolio most closely generated a 1% annualized return based on daily tracking. The goal of the competition was to provide participants with the opportunity to learn portfolio management and hedging skill. Self-assessments indicate that contestants improved their portfolio management skills and enjoyed their experience. This paper discusses methods used to generate the simulated stock and option prices and to construct the trading platform. All of the software used in the competition is being made open source in the hope that students, professors, and practitioners improve on the idea of the competition, thereby facilitating project-based learning for the future practitioners of economics, finance, and financial engineering.

Modern healthcare reforms are required to be financially, environmentally and socially sustainable in order to address the additional constraints of financial resources shrinkage, pressure to reduce the environmental impacts and demand for improving the quality of healthcare services. Decision makers face the challenge of balancing all three aspects when planning. However, implementing such an approach, particularly in healthcare, is not a trivial task. Modeling & simulation is a valuable tool for studying complex systems. This paper investigates the application of a hybrid approach that combines Agent-based Modeling & Simulation (ABMS) and Discrete-Event Simulation (DES) for analyzing sustainable planning strategies for Emergency Medical Services. The paper presents a case study that shows how combined ABMS and DES models can support strategic planning and simulation analytics, respectively. The generated data from the ABMS is fed to the DES model in order to analyze the different strategies and the preliminary results are promising.

Hybrid simulation, the combination of simulation paradigms to address a problem is becoming more popular as the problems we are presented with become more complex. This is evidenced by an increase in the number of hybrid papers published in specific domains and the number of hybrid simulation frameworks being produced across domains. This paper focuses on two hybrid simulation models from a healthcare context. The first uses system dynamics and discrete-event simulation and was developed using two separate software tools (Vensim and Simul8). The second uses agent-based and discrete-event simulation and was developed in a single software environment, Anylogic. The reflections on these models add to the debate about the viability of hybrid modelling and suggest future steps to support the take up of the approach.

A hybrid simulation model is a simulation model that is formed from at least two different simulation modelling methods (e.g., discrete event, system dynamics, agent-based). The use of different simulation modelling methods in one model requires modellers to specify additional model elements. This paper discusses three elements, namely, the modules, module interfaces and updating rules. Each module may use a different simulation method. The interface between modules defines the information that will be passed between them (including aggregation and disaggregation). The updating rules define how the information sent by one module affects other modules. These three elements are explained using a case study of a blood-supply chain simulation model for low- and middle-income countries (LMIC) which has different characteristics and challenges in comparison to the typical blood supply chain in high-income countries (HIC).

To provide automated access to multiple analysis tools, such as discrete event simulation or optimization, we extend current model-based systems engineering (MBSE) methodologies by introducing a new model to model transformation method based on object-oriented creational patterns from software design. Implemented in MATLAB’s discrete event simulation tool, SimEvents, we demonstrate the methodology by generating two distinct use cases based on a distribution supply chain and manufacturing system.

Goal-directed reproducible experimentation with simulation models is still a significant challenge. The underutilization of design of experiments, limited transparency in the collection and analysis of results, and ad-hoc adaptation of experiments as learning takes place continue to hamper reproducibility and hence cause a credibility gap. In this study, we propose a strategy that leverages the synergies between model-driven engineering, intelligent agent technology, and variability modeling to support the management of the lifecycle of a simulation experiment. Experiment design and workflow models are introduced for configurable experiment synthesis and execution. Feature-based variability modeling is used to design a family of experiments, which can be leveraged by ontology-driven software agents to configure, execute, and reproduce experiments. Online experiment adaptation is proposed as a strategy to facilitate dynamic experiment model updating as objectives shift from validation to variable screening, understanding, and optimization.

The life-cycle of a DES study goes through a number of phases, from initially goal setting to validation of experiments. Of these, the phases to support DES data collection and representation have been underrepresented in the literature to date. This paper sets out to describe a process of data collection and representation for DES within the context of the DES life-cycle. It is recognized that for large companies the data collection and representation phase differs when compared to SMEs. Due to the high complexity in performing a DES study in an SME data might not be in a DES ready format in existence whatsoever. This complexity can cost without budgets to meet it. This paper describes an expanded process in relation to the data collection and representation phase specifically for SMEs. Finally, a preliminary high level overview of a prototype is presented which supports this phase at an SME level.

Modeling large scale Web search engines (WSEs) is a complex task. It involves many issues such as representing user's behavior, query traffic, several strategies and heuristics to improve query response time, etc.. Typically, WSEs are composed of several services deployed in data centers, which must interact to get the best document results to user queries. Additionally, hardware specification like multithreading and network communications have to be taken into account. In this paper, we propose to model a service-based WSE using the Discrete Event System Specification (DEVS) formalism, which is one of the most powerful methodologies for discrete event systems. We validate our proposed model against an actual MPI implementation of the WSE and a process oriented simulation. We evaluate the accuracy of the proposed model by evaluating metrics such as query throughput and we show that there is no relevant differences, just small fluctuations less than 4%.

The fat tree topology with multipath capability has been used in many recent data center networks (DCNs) for increased bandwidth and fault tolerance. Traditional routing protocols have only limited support for multipath routing, and cannot fully utilize the available bandwidth in such networks. In this paper, we study multipath routing for fat tree networks. We formulate the problem as a linear program and prove its NP-completeness. We propose a practical solution, which takes advantage of the emerging software-defined networking paradigm. Our algorithm relies on a central controller to collect necessary network state information in order to make optimized routing decisions. We implemented the algorithm as an OpenFlow controller module and validated it with Mininet emulation. We also developed a fluid-based DCN simulator and conducted experiments, which show that our algorithm outperforms the traditional multipath algorithm based on random assignments, both in terms of increased throughput and in reduced end-to-end delay.

Agent-based modeling (ABM) approach is used to reassess the Barabasi-Albert (BA) model, the classical algorithm used to describe the emergent mechanism of scale-free networks. This approach allows for the incorporation of agent heterogeneity which is rarely considered in BA model and its extended models. The authors argue that, in social networks, people's intention to connect is not only affected by popularity, but also strongly affected by the extent of similarity. The authors propose that in forming social networks, agents are constantly balancing between instrumental and intrinsic preferences. The proposed model allows for varying the weighting of instrumental and intrinsic preferences on the agents attachment choices. The authors also find that changing preferences of individuals can lead to significant deviations from power-law degree distribution. Given the importance of intrinsic consideration in social networking, the findings emerged from this study is conducive to future studies of social networks.

The purpose of this study is to provide a user-friendly Excel simulation tool for primary care practices to manage appointment schedules which accommodate multiple patient types and stochastic service times for the nurse and provider steps in patient flow. The key features of the Excel tool are: a color-coded schedule of each provider; a Gantt chart as a visual aid for schedulers; dynamic tests of different patient mix, sequence and start time combinations; and performance measures that include average and key percentiles of wait time, idle time and session completion time. The Excel tool can be easily modified by practices to incorporate their own data and needs. In our case study, we quantify the impact of flexible nurses and/or providers to satisfy patient demands for multiple providers.

In this paper we develop a framework that integrates a discrete-event simulation model and integer programming (IP) for patient admission planning and the intermediate term allocation of resource capacities. Two types of patients are considered in this study: new and existing. The simulation model is used to find the best balance between new and existing patients arriving to each appointment time period during the day. New patients require more time to complete their admission processes and for visiting with a doctor. The IP produces an optimal calendar schedule for the doctors, i.e., the best appointment time for each doctor to see new patients. We report on computational results based on a real clinic, historical data, and both patient and management performance measures.

Oncology clinics face several complexities in their processes. When patients arrive at the infusion chairs, nurses and pharmacy technicians must be available to get the patients ready for the infusion and mix their drug treatments. This requires having the right information at the right moment. This research develops a detailed discrete event simulation model which considers the interactions between resources, information, and patient flow. The model was used to evaluate different scheduling policies and determine which of them could be incorporated in the clinic with the objective of increasing daily throughput without affecting patient wait time or total time in the system.

In this paper various Inter Terminal Transport (ITT) systems for the Port of Rotterdam are evaluated. The Port Authority is investigating possible solutions for the transport of containers between terminals at the existing Maasvlakte 1 and the new Maasvlakte 2 areas. A discrete event simulation model is presented that incorporates traffic modeling, which means that delays occurring due to traffic will have an impact on the system's performance. The model is applied to 4 different ITT vehicle configurations, including Automated Guided Vehicles (AGVs), Automated Lifting Vehicles (ALVs), Multi Trailer Systems (MTSs) and a combination of barges and trucks. Furthermore, 3 realistic demand scenarios for the year 2030 are used for the analysis.

Terminal operating systems (TOS) play a major role in today’s terminal performance. A TOS is used to create operational plans in order to ensure timely handling of vessels, trucks and trains at minimal operational cost. Creating appropriate operational plans forms a challenge for the planners, as plans should take into account a variety of aspects, such as grounding decisions and equipment dispatching. In addition, several decisions have to be made within a limited time frame, couple of hours before starting the operation. Wrong decisions in the plan can have major impact on the operation and implies financial and safety consequences. Planners face these problems only when the plans are actually executed in live operation. In this article, we propose to use a fast yet accurate simulation of both the operation and the TOS in order to support planners to investigate and adapt plans before they are applied in live operations.

Various actors are involved in hinterland transportation of incoming rail containers along the maritime transport chain. To coordinate each actors logistics processes, and therefore to improve utilization of existing transport capacity, the early provision of information, e.g. in form of estimated time of arrival (ETA), is inevitable. The objective of this paper is to measure impacts of these information flows on capacity utilization via a simulation based approach. To simulate the effect of ETA container from vessel to hinterland transport mode rail, a system dynamic simulation model is developed based on a case study about input containers at the port of Hamburg. As result the container output on rail is compared with and without ETA for different container input volumes. It will be shown, managing provision of information in supply chains – such as maritime transport chains – is a valuable approach for increasing existing utilization.

We provide a tutorial overview of simulation optimization methods, including statistical ranking \& selection (R\&S) methods such as indifference-zone procedures, optimal computing budget allocation (OCBA), and Bayesian value of information approaches; random search methods; sample average approximation (SAA); response surface methodology (RSM); and stochastic approximation (SA). We provide high-level descriptions of each of the approaches, as well as some comparisons of their characteristics and relative strengths; simple examples will be used to illustrate the different approaches in the talk. We then describe some recent research in two areas of simulation optimization: stochastic approximation and the use of direct stochastic gradients for simulation metamodels.

This paper presents the use of simulation based optimization in addressing manufacturing flow problems at a heavy equipment manufacturer. Optimizing the buffer allocation in an assembly line and optimizing the worker assignment at workstations are two independent problems addressed, with the objective to maximize throughput rate. The simulation models of the system, built using an in-house tool based on SLX, is interfaced with an custom designed meta-heuristic based on Particle Swarm Optimization (PSO). Two versions of the PSO have been developed: one with integer decision variables (for buffer space allocation) and another with binary variables (for worker assignment). The performance of the proposed simulation based optimization scheme is illustrated using case studies.

This study presents a simulation design and analysis case study of a flexible manufacturing system (FMS) considering a multi-response simulation optimization using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) based Taguchi approach. While in order to reduce expensive simulation experiments with the Taguchi design, the TOPSIS procedure is used to combine the multiple FMS responses (performance measures) into a single response in the optimization processes. Thus, TOPSIS carries out an important role to build a surrogate objective function that represents multiple responses of the system. The integrated approach finds a new design considering discrete factors (physical and operational parameters) which affect the performance measures of FMS. Optimal design configuration is obtained for the considered system with improved performance.

In a flat panel display (FPD) production line, unlike a table-type machine that processes one glass at a time, an inline cell works simultaneously on several glasses unloaded from different cassettes in a serial manner and is divided into two types (uni-inline cell and bi-inline cell) according to the job loading and unloading behavior. In order to build a production simulator for this type of FPD production line, an object-oriented event graph modeling approach is proposed where the FPD production line is simplified into a job shop consisting of two types of inline cells, and the job shop is represented as an object-oriented event graph model. This type of job shop is referred to as a heterogeneous job shop. The resulting model is realized in a production simulator using an object-oriented event graph simulator and is illustrated with the experimental results from the production simulator.

An overview is presented of SAS Simulation Studio, an object-oriented, Java-based application for building and analyzing discrete-event simulation models. We emphasize Simulation Studio's hierarchical, entity-based approach to resource modeling, which facilitates the creation of realistic simulation models for systems with complicated resource requirements, such as preemption. Also discussed are the various ways in which Simulation Studio integrates with SAS and JMP for data management, distribution fitting, and experimental design. We explore a variety of simulation models, highlighting the unique capabilities and newer features of Simulation Studio.

For close to 35 years, Arena® simulation has set the standard for simulation software. This presentation will provide an overview of Arena’s broad capabilities and easy-to-use modeling methodology that have made Arena the premier choice of academics and industries the world over.

Managers & Engineers need state-of-the-art tools to help in planning, design, and operations of their complex facilities. The AutoMod product suite from Applied Materials has been used on thousands of projects to help engineers and managers make the best decisions possible for over 30 years. Come see our exciting plans for the future of AutoMod product and why is has outlasted the competition.

Simulation software can be an effective tool to analyze and optimize process based operations. This abstract details the implementation of a warehouse simulation using the Simcad Pro dynamic simulation environment and its integrated data connectivity. The validated simulation model is then transformed to a real-time system providing live visibility, tracking, and real-time optimization of the warehouse. Details on connecting the model to RFID, Barcode, GPS, and WMS systems are also presented. Real-time visualization and optimization of the facility including; AGV behavior, picking, and replenishment are based on the real-time data received. Moreover, internal and unattended model transformation and optimization based on WMS feedback is explored.

Analyzing systems can be a complex task especially when there is feedback across several variables in the model. Formal mathematical notation makes it difficult to understand the influences of feedback and cause/effect. Forrester created the System Dynamics methodology as a means to assist in this understanding by employing a hydraulic analogy. In this methodology, variables become simulated objects such as water valves or tanks. A variety of implementations allow users to construct and simulate these models. The problem is that for many implementations, the intuitive nature of water flow, intended by the methodology, is not as clear as it could be. For instance, the rate of flow or level in a tank may not be visualized. For novices, we suggest that this issue, as well the ability to understand relationships and linking across multiple representations can be problematic. We designed and describe a web-based interface that solves these problems.

Dynamic computer simulations seek to engage the viewer by providing an intuitive representational mapping of common knowledge features to new knowledge concepts. Our research aims to provide enhanced understanding of complex systems through participatory interaction with our dynamic simulation models. Previous research has indicated that virtual and tangible models are well suited for use in informal education spaces, as they increase user interaction and curiosity amongst children and adults. We designed and implemented an interactive virtual environment as well as an interactive tangible “water computer” to represent the complex interspecies behavior of Lotka-Volterra predator-prey dynamic system. We designed our simulation models for use in informal STEM education settings, with a design focus on enhanced interactions and reflexive thinking.

We consider a semiconductor production line in which production stations are afflicted by a defect deposition process and immediately followed by an inspection step. We propose to integrate operational aspects into quality considerations by formulating a Cycle Time (CT) versus Yield trade off. We connect the two performance measures through the determination of the limit for defects at the inspection step. We extend former results to a tandem production line and present an optimal greedy algorithm that provides the Pareto-optimal set of Upper Control Limit (UCL) values for the line. The obtained model enables decision makers to knowingly sacrifice Yield to shorten CT and vice versa.

Variability is an inherent component of all production systems. To prevent variability propagation through the whole production line, variability must be constantly monitored, especially for bottleneck toolsets. In this paper, we propose measures to evaluate workload variability for a toolset configuration. Using industrial data, we show how making the toolset configuration more flexible by qualifying products on machines decreases variability. By quantifying the toolset workload variability, our variability measures makes it possible to estimate the variability reduction associated to each new qualification. The industrial results show significant workload variability reduction and capacity improvement.

A novel agent-based model, the Electricity Market Complex Adaptive System (EMCAS) Model, is designed to study market restructuring and the impact of new technologies on the power grid. The agent-based approach captures the complex interactions between the physical infrastructure and the economic behaviors of various agents operating in an electricity market. The electric power system model consists of power generating plants, transmission lines, storage technologies, and load centers. The electric power market is composed of generating company agents who bid capacity and prices into power pools administered by an Independent System Operator (ISO). The ISO agent balances supply and demand for day-ahead markets. EMCAS also simulates real-time market operation to account for the uncertainties in day-ahead forecasts and availability of generating units. This paper describes the model, its implementation, and its use to address questions of congestion management, price forecasting, market rules, and market power.

In the interest of increasing energy efficiency and avoiding higher generation costs during peak periods, utility companies adopt various demand response (DR) methods to achieve load leveling or peak reduction. DR techniques influence consumer behavior via incentives and cause them to shift peak loads to off-peak periods. In this paper we study the energy consumption behavior of residents in response to a variable real-time pricing function. We consider thermostatic loads, specifically air conditioning, as the primary load and apply the model predictive control (MPC) method to study the behavior of consumers who make consumption decision based on a trade-off between energy cost and thermal comfort. An agent-based simulation is used to model a population where each household is an agent embedded with the MPC algorithm. Each household is associated with a multi-attribute utility function, and is uniquely defined via the use of stochastic parameters in the utility function.

Regulators and policy makers, facing a complicated, fast-paced and quickly evolving marketplace, require new tools and decision aides to inform policy. Agent-based models, which are capable of capturing the organization of exchanges, intricacies of market mechanisms, and the heterogeneity of market participants, offer a powerful method for understanding the financial marketplace. To this end, we have worked to develop a flexible and adaptable agent-based model of financial markets that can be extended and applied to interesting policy questions. This paper presents the implementation of this model. In addition, it provides a small case study that demonstrates the possible uses of the model. The source code of the simulation has also been released and is available for use.

Forio's Simulate™ has been used to develop a capacity planning model enabling users to estimate media resources and costs to store data from a source into NOAA’s electronic repository, CLASS. The model has been designed to enable users - instead of the modeler - to largely control the simulation runs. In order to do this a number of challenges allowing more dynamic user control of a simulation had to be met. This paper describes how the modeling language and UI Designer were used to overcome these challenges.

Planning of potash mining operations requires consideration of many interacting machines, overlapping maintenance activities, layout of mine field as well as capacity of ore bunkers and conveyors. In this case study, an integrated low-level simulation-based tool with user-friendly interface was developed for Europe’s largest potash producer to support monthly operations planning.

In this presentation, a case of choosing best position for the new ore car with extended capacity is discussed. The presentation also discusses challenges that were faced during development of the model and shows the two libraries that were developed to overcome these challenges:

(1) Agent Graph Library that extends AnyLogic’s built-in functionality for modeling interactions of agents that are moving in networks, and

(2) Discrete-Rate Library for modeling continuous flows of materials.

The simulation-based decision support tool is used for monthly production planning, identification and avoidance of potential process bottlenecks as well as mine workers’ KPIs calculation.

Biomass is a renewable feedstock for production of biofuels, bioproducts and biopower. Securing and maintaining a reliable year-round supply of biomass that meets quality specifications at a reasonable cost is a significant barrier for conversion facilities. Simulation tools to evaluate equipment design and operational parameters aid in identifying and prioritizing R&D needs, determining required resources, and estimating costs. The Integrated Biomass Supply Analysis and Logistics (IBSAL) decision support system, developed at Oak Ridge National Laboratory for the Department of Energy, is a dynamic, discrete-event framework written in ExtendSim. IBSAL simulations were successfully used to evaluate the impact of advanced technologies demonstrated by FDC Enterprises and their project partners on reducing the delivered cost of corn stover as a bioenergy feedstock.

This tutorial explains how to develop dedicated simulators for executing event graph models and activity cycle diagram (ACD) models. An event-graph simulator template and an ACD simulator template are presented in pseudo code form, together with example C# implementations for a simple discrete-event system. A list of the simulation programs in C# codes is provided in a website. A brief description of a general-purpose simulator for executing ACD models is also presented.

A considerable amount of research on parallel discrete event simulation has been conducted over the past few decades. However, most of this research has targeted the parallel simulation infrastructure; focusing on data structures, algorithms, and synchronization methods for parallel simulation kernels. Unfortunately, distributed environments have high communication latencies that can reduce the potential performance of parallel simulations. Effective partitioning of the concurrent simulation objects of the real world models can have a large impact on the amount of network traffic necessary in the simulation, and consequently the overall performance. This paper presents our studies on profiling the characteristics of simulation models and using the collected data to perform partitioning of the models for concurrent execution. Our benchmarks show that Profile Guided Partitioning can result in dramatic performance gains in the parallel simulations. In some of the models, 5-fold improvements of the run time of the concurrently executed simulations were observed.

Microgrids (MGs) offer new technologies for semiautonomous grouping of alternative energy loads fed into a power grid in a coordinated manner. Simulations of these microgrids are time critical yet computationally demanding, inherently complex, and dynamic, especially when they are constructed for control purposes. In this paper, we address the design ranking and selection problem in MG simulations from a set of finite alternatives in the presence of stochastic constraints. Each design encapsulates a different level of control of the segregation mechanism within the system, and a performance function measured as a combination of the incurred cost and energy surety. Building on this performance function, optimal computing budget allocation (OCBA) method is used to efficiently allocate simulation replications for selecting the best design with significant accuracy and reasonable computational burden. Computational results on a multi-scale MG testbed have shown that OCBA algorithm outperforms equal and proportional to variance allocation of replications.

Systems biology studies the complex interactions of biological and biochemical systems rather than their individual molecular components. System biology simulations can be embarrassingly parallel jobs that have no dependency among individual simulation instances, and thus lend themselves to parallel execution over distributed resources to reduce their overall execution time. One example of such distributed resources is a Desktop Grid for Volunteer Computing that aims to use vast numbers of computers to support scientific applications. The SZTAKI Desktop Grid (SZDG) uses a modified form of the volunteer computing software BOINC to implement an institution-wide Desktop Grid. This paper reports on experiences of porting the SIMAP systems biology ODE simulator to SZDG. A case study using a simulation of the mammalian ErbB signaling pathway reports on significant speedup.

We develop an empirically calibrated hospital-wide simulation model to represent a time-varying, multi-server queuing network with multiple patient classes. Our main focus has been on quantifying the impact of discharge profiles to alleviate inpatient bed congestion. A discharge profile is defined by (a) discharge window, which specifies hours of the day discharges are allowed; and (b) the maximum capacity for discharges in each hour of the window. Results of our simulation model show that a more responsive discharge policy that prioritizes discharges in units with longer admission queues can significantly reduce waiting times (40% reduction in queues). In comparison, an early-in-the-day discharge policy has a lower impact on improving bed congestion; we also find that early-in-the-day discharges are very hard to realize in practice. Further, expanding the discharge windows by only 2 hours in the evening (7- 9 PM) creates the same benefit, and is more realistic.

Cardiovascular disease (CVD) is the leading cause of death in the U.S. and has placed heavy economic burden on the healthcare system. Recognizing the importance of CVD prevention, the American Heart Association (AHA) recently identified several most important risk factors of CVD and proposed a concept of ideal cardiovascular health. Based on this concept, we developed an agent-based model which is designed to capture the individual health progression and study the emergent CVD-related population health outcomes (i.e., diabetes, myocardial infarction, stroke, and death). The numerical results demonstrate the predictive validity of the model and also show how the model could be used in practice by assessing the impact of a set of hypothetical lifestyle interventions on CVD-related health outcomes. With the strength of capturing population heterogeneity and health progression stochasticity, our model is expected to help policy makers design more effective intervention programs for the population of their interest.

This paper presents a discrete event simulation-based analysis of the Radiation Therapy (RT) care delivery process at the Radiation Oncology Center of the University of North Carolina (UNC) at Chapel Hill aimed at improving process reliability and patient safety. The use of quality assurance (QA)checklists and people-based safety barriers (SBs) in radiation oncology are widely recognized methods for detecting potential human errors before they reach the patient. In this study, data on patient safety events (an incident that reached the patient, whether or not the patient was harmed) and near misses (an incident that comes close to reaching the patient but is caught and corrected beforehand) were collected through a comprehensive safety program and used to estimate event rates and reliability score for each QA and SB. Recommendations for reducing risk of events by using the most effective combination of QA/SBs is provided.

Boeing Commercial Airplanes produces four twin-aisle airplane models at its Everett, Washington production facility—the largest building by volume in the world. Efficient and effective material handling of large airplane substructures is critical to maintain production rates, and the Everett facility employs two interconnected systems of overhead cranes to move airplane sections through the factory. The crane scheduling team needed a tool to evaluate current and proposed crane schedules for feasibility, rate capability, and potential bottlenecks. Boeing Research and Technology partnered with Simio LLC to develop a simulation model of the crane network that would execute and evaluate a series of crane moves. The model employs both discrete event and agent-based paradigms to model the complex system and to allow for highly configurable initial states. This approach allows for rapid schedule evaluation, non-recurring planning, and real-time system modeling. In this paper we present the system, the model, and results.

In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing). In the proposed Iterative Simulation-Optimization (ISO) approach, we use a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities are used to introduce feedback constraints, that help exchange information between the two models. The proposed method is tested using benchmark instances from the OR library. The results indicate that the method gives near optimal schedules in a reasonable computational time.

Workers cross-trained with multiple tasks can improve the workforce flexibility for the plant to handle variations in workload. Therefore, it is necessary to study the dynamic multi-skilled workforce planning problem of production line with the application of cross-training method. The conclusion is helpful to economize the cost of human resources when workload is low and enhance the productivity in the opposite case. This paper studies the dynamic multi-skilled workforce planning problem by exploring the effect of worker pool size and cross-training level on the performance of production line through simulation. A hybrid simulation model is built as the platform to study this problem with Discrete-event Simulation (DES) method and Agent-based Simulation (ABS) method. Besides, the effect of worker learning and forgetting is inevitably considered accompanied with the introduction of cross-training method and its impact on production line will be illustrated.

Although a corporation does not own all of its supply chain, the entire chain is responsible for product delivery and customer satisfaction. As one of several methodologies available for supply chain analysis, simulation has distinct advantages and disadvantages when compared to other analysis methodologies. This tutorial will detail the reasons why one would want to use simulation as the analysis methodology to evaluate supply chains, its advantages and disadvantages against other analysis methodologies such as optimization, and business scenarios where simulation can find cost reductions that other methodologies would miss.

Machine failures are major causes of direct downtime as well as system losses (blocked and idle times) in production flows. A previous case study shows that prioritizing bottleneck machines over others has the potential to increase the throughput by about 5%. However, the bottleneck machine in a production system is not static throughout the process of production but shifts from time to time. The approach for this paper is to integrate dynamic maintenance strategies into scheduling of reactive maintenance using Discrete Event Simulation. The aim of the paper is to investigate how a shifting priority strategy could be integrated into the scheduling of reactive maintenance. The approach is applied to and evaluated in an automotive case-study, using simulation for decision support. This shows how to shift prioritization by tracking the momentary bottleneck of the system. The effect of shifting priorities for planning maintenance activities and its specific limitations is discussed.

For small-volume products in particular, U-shaped assembly systems represent an important alternative to straight-line systems. For this kind of system, staff assignment can take various forms: assignment to adjacent and opposing stations, mixed and one-piece flow assignment. In this paper, a U-shaped assembly system is defined on the basis of a known straight-line assembly, and the most suitable form of staff assignment is then determined. If the station layout is interpreted as a capacity graph, and if staff assignment is defined in the form of a staff assignment graph, the same methods can be used to solve the staff assignment problem as those used to match the precedence and capacity graphs for the purpose of line balancing. It is shown that the performance of simulated solutions sometimes varies greatly from that of static balancing solutions, in particular if staff travel times are taken into account.

Manufacturing organizations are able to accumulate large amounts of production and environmental data due to advances in data collection, communications technology, and use of standards. The challenge has shifted from collecting a sufficient amount of data to analyzing and making decisions based on the huge amount of data available. Data analytics (DA) can help understand and gain insights from the big data and in turn help advance towards the vision of smart manufacturing. Modeling and simulation have been used by manufacturers to analyze their operations and support decision making. This paper proposes multiple methods in which simulation can serve as a DA application or support other DA applications in manufacturing environment to address big data issues. An example case is discussed to demonstrate one use of simulation. In the presented case, a virtual representation of machining operations is used to generate the data required to evaluate manufacturing data analytics applications.

High resolution population distribution data is critical for successfully addressing critical issues ranging from energy and socio-environmental research to public health to homeland security. Commonly available population data from Census is constrained both in space and time and does not capture the population dynamics as functions of space and time. This imposes a significant limitation on the fidelity of event based simulation models with sensitive space-time resolution. This paper will describe ongoing development of high-resolution population distribution and dynamics models, at Oak Ridge National Laboratory, through spatial data integration and modeling with behavioral or activity-based mobility datasets for representing temporal dynamics of population. The model is resolved at 1 km resolution globally and describes the U.S. population for nighttime and daytime at 90m. In addition, we will discuss development and integration of transportation, physical and behavioral science computational approaches for applications in critical infrastructure management from local to global scales.

There is an increasing interest on using Online Social Networks (OSNs) in a wide range of applications. Two interesting problems that have received a lot of attention in OSNs is how to provide effective ways to understand and predict how users behave, and how to build accurate models for specific domains (e.g. marketing campaigns). In this context, stochastic multi-agent based simulation can be employed to reproduce the behavior observed in OSNs. Nevertheless, the first step to build an accurate behavior model is to create an agent-based system. Hence, a modeler needs not only to be effective, but also to scale up given the huge volume of streaming graph data. To tackle the above challenges, this paper proposes a MapReduce-based method to build a modeler to handle big data. By considering the Obama's Twitter network on the presidential campaign, we show experiments on how efficient and effective our proposal is.

The Financial Stability Oversight Council (FSOC) was created to identify and respond to emerging threats to the stability of the U.S. financial system. The research arm of the FSOC, the Office of Financial Research, has begun to explore agent-based models (ABMs) for measuring the emergent threat of systemic risk. We propose an ABM-based regulatory structure that incentivizes the honest participation and data contribution of regulated firms while providing clarity into the actions of the firms as endogenous to the market and driving emergent behavior. We build this scheme onto an existing ABM of a single-asset market to examine whether the structure of this scheme could provide its own benefits to market stabilization. We find that without regulatory intervention, markets acting within this proposed structure experience fewer bankruptcies and lower leverage buildup while returning larger profits for the same amount of risk.

Central European inland waterways are presently utilized way below their theoretical carrying capacity. For instance, cargo transported on the Danube is only 10-20% of that transported on the Rhine. To support an increase of transport flows on inland waterways (especially container transport on the Danube) and to contribute to a significant modal shift from road to waterways, operators have to be enabled to improve their economic position by improving the material flow in the handling points of the intermodal transport chain, container terminals, which oftentimes form a considerable bottleneck due to e.g., long processing times. In this paper, gaps in the material flow of container terminals alongside the Danube are revealed with the use of simulation. The Simulation enables the terminal operator to create an experimental model and decide on the best recommended course of action.

As the bottleneck of the shipping routes from the Indian Ocean to the Pacific Ocean, the Singapore Strait is handling high daily traffic volume. In order to enhance navigational safety and reduce collisions at sea, several approaches have been proposed. However, most of the contributions adopt deterministic algorithms, failing to consider the stochasticity due to human behaviors of ship captains. Moreover, the effectiveness of these approaches is hindered by the fact that their focus is on providing a globally optimal safe set of trajectories to all vessels involved in encounter situations, almost neglecting each captain’s perspective. We propose Safe Navigation Assistance Tool (SNAT), a simulation–based search algorithm to assist the captain by suggesting highly safe and robust maneuver strategies for conflict avoidance. Extensive numerical experimentation were performed proving the effectiveness of SNAT in reducing the number of conflicts, with respect to real data provided by the Automatic Identification System (AIS).

As the global container logistics, especially the transshipment services, keep increasing, substantial improvements on both port storage capacity and port throughput rate are necessary. Besides, the future challenges of getting skilled labor and the rising labor cost have been bothering port operators. Automated Container Terminals (ACT) is a promising solution to these challenges. This study firstly introduced two new conceptual transshipment hub designs, i.e. GRID-ACT and FB-ACT. Then the simulation models were designed respectively and the analytical results revealed both pros and cons for both systems. Besides, the land utilization and capacity of two ACTs have also compared among advanced contemporary ports in the world.

Many procedures have been proposed in the literature to select the best from a finite set of alternatives. Among these procedures, frequentist procedures are typically designed under an indifference-zone (IZ) formulation, where an IZ parameter needs to be specified by users at the beginning of procedure. The IZ parameter is the smallest difference in the performance measure that the users care. In practice, however, the IZ parameter is often difficult to specify appropriately and may be specified in a conservative way (thus leading to excessive sampling effort). In this paper, we propose a frequentist IZ-free selection-of-the-best procedure. The procedure guarantees to select the best with at least a pre-specified probability of correct selection in an asymptotic regime. Through numerical studies, we show our procedure may out-perform IZ procedures, in terms of total sample size, when the IZ parameter is set conservatively or there are a large number of alternatives.

We consider the problem of identifying the system with the largest expected mean among a number of simulated systems. We provide a new fully sequential procedure whose continuation region is developed based on multivariate Brownian motion when the variances of the systems are known and equal. We provide an approximation to determine the procedure parameters and we show experimental results.

Traditional solutions to ranking and selection problems include two-stage procedures (e.g., the NSGS procedure of Nelson et al. 2001) and fully-sequential screening procedures (e.g., Kim and Nelson 2001 and Hong 2006). In a parallel computing environment, a naively-parallelized NSGS procedure may require more simulation replications than a sequential screening procedure such as that of Ni, Hunter, and Henderson (2013) (NHH), but requires less communication since there is no periodic screening. The parallel procedure NHH may require less simulation replications overall, but requires more communication to implement periodic screening. We numerically explore the trade-offs between these two procedures on a parallel computing platform. In particular, we discuss their statistical validity, efficiency, and implementation, including communication and load-balancing. Inspired by the comparison results, we propose a framework for hybrid procedures that may further reduce simulation cost or guarantee to select a good system when multiple systems are clustered near the best.

Distributed simulations provide a powerful framework for utilizing a greater amount of computing resources, but require that consideration be taken to ensure that the results of these simulations match results that would have been produced by a single sequential resource. Also, synchronization among the multiple nodes must also occur to ensure that events processed in the simulation maintain a timestamped order across all nodes. This work uses the popular network simulator ns-3. The ns-3 simulator is a discrete event network simulator used for educational and research-oriented purposes which provides two implementation options for distributed simulations based on the null message and granted time window algorithms. We examine the performance of both distributed schedulers available in ns-3 in an effort to gauge specific features of an overall distributed network topology that warrant the use of one synchronization method over the other.

MPI collective operations are a critical and frequently used part of most MPI-based large-scale scientific applications. In previous work, we have enabled Rensselaer Optimistic Simulation System (ROSS) to predict the performance of MPI point-to-point messaging on high-fidelity million-node network simulations of torus and dragonfly interconnects. The main contribution of this work is an extension of these torus and dragonfly network models to support MPI collective communication operations using the optimistic event scheduling capability of ROSS. We demonstrate that both small- and large-scale ROSS collective communication models can execute efficiency on massively parallel architectures. We validate the results of our collective communication model against the measurements from IBM Blue Gene/Q and Cray XC30 platforms using a data-driven approach on our network simulations. We also perform experiments to explore the impact of tree degree on the performance of collective communication operations in large-scale network models.

As many other kinds of simulation experiments, simulations of computer networks tend to generate high volumes of output data. While the collection and the statistical processing of these data are challenges in and of themselves, creating meaningful visualizations from them is as much an art as it is a science. A sophisticated body of knowledge in information design and data visualization has been developed and continues to evolve. However, many of the visualizations created by the network simulation community tend to be less than optimal at creating compelling, informative narratives from experimental output data. The primary contribution of this paper is to explore some of the design dimensions in visualization and some advances in the field that are applicable to network simulation. We also discuss developments in the creation of the visualization subsystem in the Simulation Automation Framework for Experiments (SAFE) in the context of best practices for data visualization.

Energy efficient building design demands a complete understanding of building envelope heat transfer along with airflow behavior. Although existing building energy modeling tools provide 2D heat transfer analysis, they fail to execute full-scale 3D heat transfer analysis and lack proper integration with Building Information Modeling BIM tools. This paper addresses these issues first by developing a BIM-integrated plugin tool to extract building geometry and material information from a 3D building model and then demonstrating a complete 3D heat transfer analysis along with grid generation. This paper discusses the preliminary research work in data extraction from Building Information Modeling (BIM) for performing 3D heat transfer in a seamless manner. This approach will help towards the implementation of a 3D heat transfer in Dynamic-BIM Workbench, an integrative, collaborative, and extensible environment. This Workbench enables integration of domain modeling, simulation, and visualization.

The purpose of this research is to provide a lifecycle building sustainability evaluation method to guide different stakeholders in how to apply sustainable practices and maintain the expected sustainability. Building Information Modeling (BIM) is selected to be a platform to integrate all the information to improve interoperability. Green standards are embedded in the BIM model and a rule-based system is developed to automatically evaluate the design and the building performance. Data are collected by using a Real-Time Location System (RTLS) and are used to update the BIM model. The as-built model is checked to see if it matches the sustainability aspects regarding the construction processes. During operation, energy consumption data are collected and analyzed. The performance of the building is checked to see if the designed features reach the sustainability goals. By integrating the BIM, RTLS, and other information, a prototype system of lifecycle sustainability evaluation is developed and tested.

Net zero energy is a topic that is trending in the construction industry. A part of the net zero movement garnering the most attention is K-12 public school construction. Alachua County’s Meadowbrook Elementary School (K-5) is a high performance school which can achieve net zero energy status with some proven and effective practices. In this paper, we discuss and compare the current baseline energy usage of the school since its completion and target opportunities to reduce energy usage. Recommendations based on the ASHRAE Advanced Energy Design Guide (50% Energy Savings) with the help of energy modelling and simulation would close the gap needed to make Florida schools energy self-sufficient. Further renewable energy production will be added by taking advantage of the Florida climate zone. The suggestions reviewed and applied in this paper will establish guidelines for prospective net zero energy schools in general and the Florida based schools in particular.

Spatial statistical models are of considerable practical and theoretical interest. However, there has been little work on rare-event probability estimation for such models. In this paper we present a conditional Monte Carlo algorithm for the estimation of the probability that random graphs related to Bernoulli and continuum percolation are connected. Numerical results are presented showing that the conditional Monte Carlo estimators significantly outperform the crude simulation estimators.

A key challenge for an efficient splitting technique is defining the importance function. If the rare event set consists of multiple separated subsets this challenge becomes bigger since the most likely path to the rare event set may be very different from the most likely path to an intermediate level. We propose to mitigate this problem of path deviation by estimating the subset probabilities separately using a modified splitting technique. We compare the proposed separated splitting technique with a standard splitting technique by estimating the probability of entering either of two separated intervals on the real line. The squared relative error of the estimator is shown to be significantly higher when using standard splitting than when using separated splitting. We show that this difference increases if the rare event probability becomes smaller, illustrating the advantage of the separated splitting technique.

In this paper, we consider rare-event simulation of the tail probabilities of Gaussian random fields. In particular, we design importance sampling estimators that are uniformly efficient for a family of Gaussian random fields with different mean and variance functions.

In this session MOSIMTEC introduce attendees to the basic principles of object-oriented modeling, design, and implementation of simulation models. Object-oriented-programming (OOP) allows for developing models faster, reusing models and components, and lower cost of ownership. We’ll review how OOP is being applied in modern discrete-event-based simulation packages and how a simulationist can take full advantage of object-oriented-programming for enhanced system abstraction.

Stella was introduced in 1985 to make the process of creating simulation models faster and more intuitive. Originally designed to represent continuous systems, functionality has been added to address discrete elements including queues for managing arrival and processing coordination, conveyors for handling material transit with loss, and ovens for handling batch processing. Because the models created do not require detail around every element in a processing chain, they are faster to create and analyze than fully specified discrete event simulations would be. This makes Stella ideal for situations in which full detail on all stations and items being processed is not necessary, and allows the models developed to more easily include business concepts such as profit, loss and competitive position. In this presentation, we will demonstrate how our newest product, Stella Professional, can be used to build, simulate and analyze process oriented models.

MATLAB is a platform for simulation, analysis and optimization. We will show what’s new in MATLAB, Simulink (time-based simulation), Stateflow (state transition diagrams) and SimEvents (discrete-event simulation) and how they can be used together to model and analyze operations.

In case of a fire emergency a set of resources are mobilized to response to the emergency. Depending on each emergency scenario the mobilized resources are placed during several days forcing to create shifts to relay the personnel. The vehicles placed in the emergency locations cannot always be used since they are required to perform more critical tasks. This problem can be modelled with a Vehicle Route Problem Pickup and Delivery with Time Windows (VRPPDTW) adding a set of new restrictions. Different stochastic variables have critical influence on the performance of the transportation orders. The most important is the pick-up since huge delays appear due to firemen maneuvers at the fire front that cannot be interrupted or travelling time from the fire front takes longer due to off-road conditions. A simulation model in combination to an optimization model helps on reducing the search space orienting it and removing infeasible solutions.

This paper provides an agent-based analytical framework for analyzing knowledge management policies in service-oriented organizations. The knowledge of workers dynamically changes as various services are created through the service interactions between workers and customers. Our previous study focused on the knowledge dynamics and proposed an agent-based model to discuss effective management policies in a specific customer center. However the model is unable to analyze problem situations in other types of organizations directly. In this research we investigate service characteristics and various types of business, and then construct the framework including essential model components for representing service interactions and organizational learning mechanisms in service-oriented organizations. The framework enables analysts easily to build an agent-based model by selecting model components based on the features of their target organization. The simulation with the framework can provide an enormous volume of useful information about possible organizational changes and workers’ behavior for their decision making.

Adoption of competing platforms, such as video game consoles, is usually explained retrospectively with cumulative, direct (overall quantity of other players) and indirect network effects (overall quantity of games). An agent-based model, fed with big data representing the competition between PlayStation 3 and Xbox 360, shows that the quality of the network effects explain adoption more accurately than the mere strength of these effects. Instead of choosing a console with the strongest network effects (highest amount of other players and games), players choose the console with the highest quality of direct network effects (amount of friends among the players) and indirect network effects (amount of games that are aligned with the preferences of the player).

Agent-based modeling is one of the promising modeling tools that can be used in the study of population dynamics. Two of the main obstacles hindering the use of agent-based simulation in practice are its scalability when the analysis requires large-scale models such as policy studies, and its ease-of-use especially for users with no programming experience. While there has been a significant work on the scalability issue, ease-of-use aspect has not been addressed in the same intensity. This paper presents a graphical user interface designed for a simulation tool which allows modelers with no programming background to specify agent-based demographic models and run them on parallel environments. The interface eases the definition of models to describe individual and group dynamics processes with both qualitative and quantitative data. The main advantage is to allow users to transparently run the models on high performance computing infrastructures.

This extended abstract presents general method formats (MFs) as a continuation of the exploration of theoretical components of multi-method M&S approach. An MF can be defined as a basic arrangement of methods and their relations overlaid with system and/or phenomena. Transitions toward a format must seek justifications in order to increase research objectivity and transparency.

For models that include spatial aspects, the description and recognition of spatio-temporal patterns is an important building block for the analysis of simulation trajectories. We propose an approach that makes use of user-definable qualitative spatial relations between moving entities to represent simulation trajectories as directed labeled graph. In this graph, spatio-temporal patterns can be found through a graph pattern matching algorithm. We implemented the approach using the graph database Neo4j and successfully tested it on movement data from the Robocup Soccer Simulation as well as spatial cell biological simulations.

Empirical game theory experiments attempt to estimate causal effects of institutional factors on behavioral outcomes by systematically varying the rules of the game with human participants motivated by financial incentives. I developed a computational simulation analog of empirical game experiments that facilitates investigating institutional design questions. Given the full control the artificial laboratory affords, simulated experiments can more reliably implement experimental designs. I compiled a large database of decisions from a variety of repeated social dilemma experiments, developed a statistical model that predicted individual-level decisions in a held-out test dataset with 90% accuracy, and implemented the model in agent-based simulations where I apply constrained optimization techniques to designing games -- and by theoretical extension, institutions -- that maximize cooperation levels. This presentation describes the methodology, preliminary findings, and future applications to applied simulation models as part of ongoing multi-disciplinary projects studying decision-making under social and environmental uncertainty.

In this work, we raise three critical questions that must be investigated to ameliorate composability of virtual simulation models and to enable adoption of systematic and stringent real-time techniques to enable more scalable simulation models for virtual and constructive simulation. The real-time techniques in question enable us to separate between policies and mechanisms and, thus, the simulation engine can decide dynamically how to run the simulation given the existing resources (e.g., processor) and the goals of the simulation (e.g., sufficient fidelity in terms of timing and accuracy). The three critical questions are: (i) how to design efficient and effective algorithms for making dynamic simulation model design decisions during simulation; (ii) how to map simulation entities (e.g., agents) into (real-time) tasks; and (iii) how to enable a divide and conquer approach to validating simulation models.

Distributed computing facilitates very large scale agent-based models. However, reliable and efficient communication strategies are needed to synchronize agent states across multiple processors. Traditional management methods are conservative in nature and perform a complete synchronization of all agent state information at every time step of the simulation. An alternative approach to traditional methods is proposed which uses an event-driven technique to synchronize agents. This procedure only synchronizes changes to pertinent information in the model at each time step. This technique requires less information to be broadcast which reduces the run time of the simulation while maintaining consistency in the model.

Over the past decade, consumer interest in regionally-produced food has grown significantly. Small- and medium-scale food producers, which lack the necessary scale to satisfy large-scale distributor volume and price point requirements, can benefit by selling to regional customers through food hubs. One of the many challenges that food hubs face is determining appropriate policies for supplier management. To assess the effects of different policies on regional food system outcomes, we have developed a multi-agent simulation model of a theoretical regional food system in which farmer agents and a food hub agent iteratively negotiate, trade, evaluate outcomes, and adapt their strategies based on these outcomes. The model captures individual and system performance measures and illustrates trade-offs associated with each policy.

Heart disease is the leading cause of death, disability and medical spending in the United States. Technological advances have significantly improved the health outcomes of patients with coronary heart disease (CHD) but have also increased costs. However, misaligned incentives have resulted in widespread underuse of low cost, high benefit therapies (e.g. beta blockers and statins) and overuse of high cost, low benefit therapies (e.g. elective percutaneous coronary interventions). We use agent-based modeling (ABM) to explore the health and economic impact of changing the financial incentives (out-of-pocket costs) faced by Medicare patients with CHD. Using NetLogo, the patient’s life course is simulated to determine the likelihood of heart attacks and death. Financial incentives impact the use of key therapies, which impacts the likelihood of events. The model will enable policy makers to identify the most cost-effective incentive policies—i.e., to maximize the health improvements obtained for the money spent.

Increases in global energy consumption rates are substantially driven by human activities. Influencing individuals to adopt energy-saving practices in their daily routines is fundamental to every energy-conserving intervention. Studies have shown that widespread diffusion of practices in a population requires reinforcements from influentials, in particular opinion leaders. However, the level of influence of opinion leaders in large scale diffusions of energy conservation practices is unknown. To address this gap, we developed an agent-based simulation model based on empirical communication data on energy conservation from Twitter. Using global sensitivity analysis, we explored the patterns of information diffusion—and, by extension, the flow of influence—in large networks, and identified the key attributes that dominate the opinion leaders’ influence. We found interventions that focus on these attributes can increase the level of influence from opinion leaders, which can lead to faster and wider dissemination of energy conservation practices in large online networks.

The ongoing efforts for ever increasing automation of the contemporary production and service systems are usually challenged by the lack of a well-developed formal theory for managing the representational and computational complexities that result from the large-scale nature and the operational complexity of the corresponding applications. Currently, these complexities are addressed by further structural and behavioral restrictions imposed at the design level, which simplify the design process but also render the final system quite rigid, and therefore, inflexible and inefficient. The presented research program seeks to address these limitations by (i) abstracting the considered systems to a class of resource allocation systems (RAS), and (ii) developing effective and efficient control policies for these RAS through the employment of qualitative and quantitative Discrete Event System (DES) theory. The derived results are applied to the scheduling of capacitated re-entrant lines, i.e., re-entrant production lines with finite buffers at their workstations.

Hybrid algorithms are a promising approach to speed-up the execution of multi-scale biochemical reaction networks, i.e., networks with reactions that operate on different time scales. The basic idea is to use the quasi-steady state distribution of the fast reactions, computed either analytically or empirically, to update the propensities of slow reactions and to apply a stochastic simulation algorithm to compute the slow reactions. We apply this approach to multi-level models. Executing multi-level models that are characterized by dynamic nested structures by these hybrid algorithms poses specific challenges. For example, all reactions, even fast reactions, can change the structure of the model and consequently the set of reactions. To evaluate our approach, we use the rule-based multi-level language ML-Rules.

In this poster, we evaluate the effectiveness of four kriging-based approaches for simulation optimization with homogeneous noise: Augmented Expected Improvement (AEI), Approximate Knowledge Gradient (AKG), the Two-stage sequential optimization method (TSSO), and the well-known Efficient Global Optimization (EGO) algorithm. We test the performance of these algorithms on test functions with homogeneous noise, assuming given computing budget constraints (i.e., given number of infill points and replication budget). Our results indicate that, as long as we have enough replication budget to implement both stages of the Two-stage algorithm, this method is highly competitive, providing similar or better performance particularly in settings with high noise and many infill points.

Real world data sets often contain disproportionate sample sizes of observed groups making the task of prediction algorithms very difficult. One of the many ways to combat inherit bias from class imbalance data is to perform re-sampling. In this paper we discuss two popular re-sampling approaches proposed in literature, Synthetic Minority Over-sampling Technique (SMOTE) and Propensity Score Matching (PSM) as well as a novel approach referred to as Over-sampling Using Propensity Scores (OUPS). Using simulation we conduct experiments that result in statistical improvement in accuracy and sensitivity by using OUPS over both SMOTE and PSM.

This paper estimates simulation metamodels by B-splines with a penalty on high-order finite differences of the coefficients of adjacent B-splines. The penalty prevents overfitting. The simulation output is assumed to be nonnegative. The nonnegative spline simulation metamodel is casted as a second-order cone programming problem, which can be solved efficiently by modern optimization techniques. The method is implemented in MATLAB.

The use of fancy graphics can have a mesmerizing effect on simulation novices and, as such, may blind the user to the actual capabilities and valid usages. The visualization of simulations are distinct from other computer graphic media because they are a representation of an abstract of reality (the model) as opposed to a representation of reality. This extended abstract provides examples of visualization usages that could have a potential negative impact on users’ understanding of the simulation. The associated poster will continue on to discuss some of the possible solutions to the issues of poor visual rhetorical choices and provides an aid for non-experts in the form of a “cheat sheet” designed to allow a non-expert user insight into some visualization problems and tricks. The focus on non-experts stems from these authors’ belief that simulation users, and not the experts, will shape the ultimate future of Modeling and Simulation.

We consider the intra-day forecasting of call center arrivals, a real-time challenge faced by call center managers in practice, under the dynamic doubly stochastic Poisson process model. This model stipulates that the randomness of the arrival rate is dynamically evolving rather than static as many existing models do. A major difficulty associated with the model is to estimate the posterior probability distribution of the arrival rate given the observed arrival counts and update the forecasts in a sequential manner. In this paper, we apply the sequential Monte Carlo method to solve this computational challenge.

This paper presents an integrated software environment named HIT-SEDAES (Harbin Institute of Technology-Simulation Experiment Design, Analysis and Evaluation System). HIT-SEDAES can aid users to design simulation experiments, monitor and control simulation process, manage and analyze simulation data, evaluate system effectiveness/performance and simulation credibility. During the software environment design and implementation, several methods were applied, including the intelligent method of simulation experiment design, flexible evaluation method of operational effectiveness, multivariate simulation result validation method and simulation optimization method based on meta-model and intelligent algorithm. The software environment was well used and endorsed by some institutes.

We develop a new measure of reliability for the mean behavior of a process by calculating the probability that the cumulative sample mean will ever deviate from its long-term mean, and its true mean, over a period of time. This measure can be used as an alternative to estimating system performance using confidence intervals. We derive the tradeoffs between four critical parameters for this measure: the underlying variance of the data, the starting sample size of a procedure, and the precision and confidence in the result.

Graphics Processing Units (GPUs) are more and more used for general purpose calculations. In the area of modeling and simulation, GPU's calculations are typically associated with executing specific simulation models. Besides this application, we propose the use of GPUs not only in the context of model execution but also to analyze simulation results, e.g., to compute the similarity of simulation trajectories. We present initial evaluation results from using the GPU for such applications and discuss opportunities, challenges, and pitfalls. We conclude with further possibilities as well as some future directions for leveraging the GPU for different analyzing tasks in the field of modeling and simulation.

Interactive visual steering of a complex simulation is often limited by our inability, due to the cognitive overload, to fully grasp the data presented on a computer screen. Our cognitive processes are dependent on how our body interacts with the world (affordances) and how we off-load cognitive work onto our physical surrounding (embodied cognition). We present an approach to embodied interactive visual steering that takes advantages of affordances and embodied cognition in a large physical space. The initial implementation of the proposed approach uses augmented reality and motion tracking to display the simulated system on a scale that can benefit from embodied cognition. Embodied interactions support visual steering, i.e., changing the simulation system parameters, by using physical devices and device-embodied tasks.

Optimization by similarity is a concept under development in our research group in which a function with known optimality characteristics is matched against experimental data to determine the region where optimality could occur. If instead of using experimental data, one uses simulated data generated with an experimental design, a simulation-optimization by similarity technique is a feasible possibility. This work explores these first ideas with designs of experiments ranging from 2 independent variables to 50 independent variables.

We consider the problem of identifying the solution to an optimization problem whose domain is a subset of the mixed-integer space and whose objective and constraint functions can only be observed via a stochastic simulation. In particular, we present cgR-SPLINE, a provably efficient algorithm on integer lattices. We additionally provide heuristics for algorithm parameter selection that have demonstrated good finite-time performance of cgR-SPLINE. Lastly, we present an extension of cgR-SPLINE for mixed-integer spaces and provide conjectures on the performance of the proposed algorithm.

Prenatal screening for Down syndrome (DS) based on biomarker levels provides expectant parents with an estimated risk of a DS baby. The risk cutoff value of a prenatal screening test determines the detection and false positive rates and the selection of follow-up procedures, such as an invasive diagnostic test. Women who consider prenatal screening might face two undesirable outcomes: undetected DS live births (DSL) and procedure-related fetal losses (EFL). One-size-fits-all risk cutoff values, such as 1/270, are commonly used in DS screening to recommend diagnostic tests. However, evidence suggests that different women have different preferences about the pregnancy outcomes. The objective of this study is to find the optimal risk cutoff values for DS screening. As no closed-form solutions exist, we use Monte Carlo simulation to solve the proposed model. We find that age-specific risk cutoff values outperform one-size-fits-all risk cutoff values.

We consider a field service system with equipment located in a geographic area. The area is divided into two territories, each with a single server who provides onsite service. Since the arrival of requests for service calls, the travel time to customer location, and on site repair time are all random variables, and minimizing response time is one of the primary objectives in field services, it is a common practice to re-deploy servers between territories to control the response time. The objective of this paper is to investigate the conditions for server sharing between the two service territories. In particular, we use simulation to investigate the impact of additional travel time and server utilization on server sharing decisions between two territories.

It is believed that that frequency of analgesic demand in patient controlled analgesia (PCA) reflects the level of patient’s pain. In this paper we assume that the patient’s demand is a random process with a unique shape and parameters. In order to find this process we investigated two randomly selected, real data based morphine and fentanyl PCA logs. Based on this data we created patients behavioral model that approximated real demand data. We used the created patient behavioral models to simulate the risk of drug concentration exceeding critical threshold. 500 virtual PCA logs of both morphine and fentanyl analgesia were created. These logs allowed pharmacokinetic simulation of the effect compartment concentration. We used quantized state system model to create hybrid aggregate model of PCA. The proposed methodology allows an estimation of frequency and duration of critical episodes. These estimations might be used to evaluate patient risk of postoperative opiate overdose.

Short cycle time prediction is a well-documented problem in complex-process manufacturing such as semiconductor manufacturing. In general, the amount of production in wafer fabrication is dependent on bottleneck facilities. In this paper, we compare scheduling methods to increase the production of the wafer fabrication. We model the process based on DEVS (Discrete Event System)/SES (System Entity Structure) and estimate our methods whose models are created from the SES through pruning processes.

Every day 100 000 passengers pass through Paris Roissy Charles de Gaulle airport (CDG), making it the second HUB in Europe for passengers. Half of these passengers are connecting travelers arriving from a landing plane and having to catch an outgoing plane through a complex set of walkways, escalators, security control, bus and shuttle network. This poster shows how 1Point2 used simulation linked with a passenger and traffic database to produce a predictive simulation model of passenger flows inside airport, following individually each passenger from plane landing or airport arrival to connection plane take off or airport exit to Paris city. We show how the ergonomic interface for end user, along with database links with model for input and output data helped airport operator to reduce missed connection percentage and plan airport modification and organization, season after season.

The importance of proper verification and validation (V&V) of simulation models and experiments is well accepted in the modeling and simulation community. There is currently a push to improve the scientific aspects of modeling and simulation, including validation. To determine the amount of validation performed in practice, we analyze 110 health care simulation papers from the last eight years of the Winter Simulation Conference for the level of verification and validation reported and the types of validation techniques applied. Our results show that validation is not discussed sufficiently in most published papers. Although many authors are performing some level of validation on their simulation, close to a fourth of the papers do not mention V&V, while more than half do not provide detail of the type and amount of V&V performed.

To control air pollution and promote green production/service, China has established regional emission trade markets at several “trial cities”. These systems operate under the conditions of “Cap and Trade”. Participating companies are restricted in total greenhouse-gas-emission through initial allocation of emission quotes; but allowed to purchase emission quotes to offset their needs via a market system. Alternatively they can conduct self-purification to reduce emission and sell the surplus to gain revenue via the market. There are various risks associated with these decisions, e.g. fluctuation of market EQ price and cost of green improvement. The companies’ decisions are individually made and together they impact market’s overall behavior. The interaction between many decision makers and overall market performance is quite complex. This research apply a multi-agent based approach to build a simulation model to analyze the emission market’s performance under different risk profiles with respect to the criteria set by policy makers.

Under “Cap and Trade” conditions, a manufacturer is restricted in total greenhouse-gas-emission through initial allocation of emission quotes (EQ); but allowed to purchase emission quotes (i.e. commercialized permits for emitting certain pollutant) to satisfy additional needs via a market system. Alternatively it can make green improvement to reduce emission level to satisfy its emission needs and sell the surplus (e.g. in the form of certified-emission-quotes) to gain revenue via the market. There are various risks associated with these decisions, e.g. fluctuation of market EQ price and changing cost of green improvement. The complicated interactions between decision variables and influencing factors, coupled with process dynamics and various uncertainties associated with different risk profiles, make the decision-making process and the evaluation of solutions extremely difficult. This research proposed a discrete-event simulation based approach to analyze risk factors and characterize manufacturing system’s performance under different risk profiles commonly associated with a Cap-and-Trade setting.

The development of networks for converged services including voice, video and data over the same infra-structure requires appropriate planning and dimensioning. This work presents a methodology, based on discrete event simulation, that supports the dimensioning and planning of IP multi-service links with QoS requirements. One key aspect is the type of traffic considered: stream, which has strict time and bandwidth requirements (e.g., VoIP), and elastic, which tolerates a certain delay (e.g., FTP, TELNET and HTTP). Another key aspect is the validation of the simulation model. In the absence of actual data, an approximate and proven analytical model was used for comparison and validation. The validated simulation model can be incremented step-by-step in order to be used in more complex scenarios. Although we show the application of the method in a predefined case for the elastic traffic, the steps to be followed in a more general case are quite similar.

Ururau is a free and open-source software written in the Java programming language. The Ururau is able to develop models of simulation at any of the component layers of the software structure. This means that models can be constructed on the layer of the library (lower level), in the core of the software (new palettes, for example) or top layer of the graphical interface. Then, if you want to build simulation models with non-commercial software, with the option to either write its code both in Java as in a graphical user interface of simple operation, you have the option of using Ururau.

Running simulation experiments often requires significant amounts of computations. Public computing clouds are a cost-efficient way to purchase computing power on-demand. In particular Amazon offers a spot pricing mechanism for its public cloud service called EC2. This model has a very attractive pricing but does not guarantee uninterrupted computations. Moreover, in order to maximize their benefits the users have to be active players. In our work we develop a simulator of Amazon EC2 spot price market. We use the simulator to show that there is a trade-off between the computation cost and the computation time.

Today, vast volumes of goods are transported all over the world in containers. Customs authorities are charged with detecting smuggling and can find indications of this by screening documentation on containers that is by law provided by shippers and carriers. In the Contain project, Decision Support Systems for customs to do this risk profiling are developed. In order to test these systems, we have developed a tool (ENS-simulator) to provide simulated input to the profiling tool. In this paper, we present the simulation tool and describe the method used for generating a high rate of messages in a realistic way, to represent a typical message inflow at a large customs risk assessment center.

We consider a two-stage stochastic staffing problem for multi-skill call centers. The objective is to minimize the total cost of agents under a chance constraint, defined over the randomness of the arrival rates, to meet all the expected service level targets. First, we determine an initial staffing based on an imperfect forecast. Then, this staffing is corrected by applying recourse when the forecast becomes more accurate. We consider the recourse actions of adding or removing agents at the price of some penalty costs. We present a method that combines simulation with integer or linear programming and cut generation.

A bucket brigade strategy allows workloads in warehouses to be distributed with a minimal level of managerial planning and oversight. However, the variability and uncertainty of the pick locations within a particular order or batch often results in picker blocking and subsequent losses in productivity. This study formulates an order batching model for robust blocking control of dynamic bucket brigade OPSs. The Indexed Batching Model for Bucket brigades (IBMB) is composed of indexed batching constraints, bucket brigade picker blocking constraints, and release-time updating constraints. We show that the model minimizes the total retrieval time and improves picker utilization as much as ten percent across diverse and practical order picking situations.

Traffic density is increasing in many regions with the increasing suburbanization. The resulting congestion necessitates the need for traffic simulation, i.e. a powerful tool for investigating properties of the infrastructure such as resiliency and scalability, to make more informed decisions regarding traffic routing or some unforeseen events. Distributing traffic simulation is a promising way of reducing the computationally intensive nature of these simulations. This however is a challenge given the scale of the cities and hence finding good partitioning schemes is complicated. In this paper, we compare nine different partitioning algorithms, belonging to three families: space and graph partitioning, and a hybrid approach. We evaluate these algorithms on six large world cities and present a detailed comparison of the three major approaches. We find that a Simulated Annealing approach based on local edge labelling, performs the best.

The deferral of elective surgery is an underestimated problem in health care services. Literature reports cancellation as the usual procedure to deal with this, even though patient health is at risk with such decision, since elective does not mean optional, and not enough attention is paid to cancelled procedures and its consequences, this is the problem studied in this article. The main result of this research is a simulation model using System Dynamics, which represents the behavior of elective surgery deferral. This model is used to conduct an analysis of relationship among modeled variables to highlight main reasons of elective surgery deferral. The developed model probed to have potential to become a tool to assist on design and enhancement of surgical service strategies.

The planning of large excavation operations requires careful consideration of the conditions of the site, the soil to be excavated, and the equipment to be used. Previous research in this area addressed the macro-level or micro-level path planning of excavation operations. However, this research is integrating both macro and micro path planning issues and considering safety at the level of equipment fleet. This paper aims to: (1) synthesize a new approach for integrating macro and micro path planning methods considering the safety of excavation operations, and (2) simulate these methods using a game engine. The Unity3D game engine was used to develop a simulation environment for earthmoving operations using A* algorithm for macro path planning, and a parametric rule-based approach and RRT algorithm for micro path planning.

Past simulation studies of the multimodal freight transportation system have been unable to dynamically couple the various modes into one model; therefore, they are limited in their ability to inform on dynamic system level interactions. This paper presents a dynamically coupled multimodal transportation system operating at multiple spatial references and temporal scales. Specifically, this paper shows a dynamically coupled railroad network which closely follows major CSX railroads from Chicago, Washington DC, and Miami into the southern U.S. Ports of Savannah, Jacksonville, and Charleston. The models were developed using Arena® simulation software.

Upgrading refrigerators is one of the strategies for increasing energy efficiency in the residential sector in Colombia. We examine alternative policies for promoting the substitution of low efficiency for higher efficiency models that will reduce power consumption and CO2 emissions. The evaluated policies include increasing awareness of efficiency labels, rebates and tax reductions. We simulate the impact of these policies combining discrete choice and dynamic diffusion models. Our results show that simultaneous application of financial incentives and information programs over a 20 year period can reduce power consumption and carbon emissions by more than 174,000 GW/h and 50,000 Ton CO2 with respect to the current program.

This paper identifies construction constraints for a constraint simulation of a construction flow. Therefore the construction environment and the methodologies of scheduling in construction are analyzed. Typical characteristics of construction schedules are classified. The relationship between different activities or between activities and building elements or between different building elements are examples for identified classes. With these characteristic construction schedules of real construction projects are analyzed. The results of this survey of construction schedules and the identified strategies of construction methods are presented in this paper in order to understand the process of scheduling. Based on that, the results of constraint based scheduling simulation can be improved a lot. Additionally, the reliability of construction schedules can be improved. As a result, the productivity in construction can be increased.

Due to high costs, ambulance providers staff for the daily routine. In the case of a demand outlier, e.g., a large scale incident, EMS run out of resources and an additional source of medical aid must become operational at the incident location. The Dutch Ministry of Safety and Justice asked the Netherlands Red Cross to have a nation wide volunteer-based service operational in January 2016 that can handle the treatment of victims with minor urgencies. We designed a simulation model for the NRC to calculate both the reliability for the response time threshold of 45 minutes for the first operational team, and the number of low priority victims treated within the first 2 hours, given the number of volunteers in the region. For each potential incident location, we determine an optimal meeting point for the team of volunteers before heading to the incident location. We also provide tooling.

Model complexity constantly increases in scientific and engineering applications. To be able to implement models and simulators efficiently and accuracy modularization and reusability are key features. In this paper a combination of different approaches is combined to decrease modelling complexity. Inspired by paradigms used in professional software engineering a methodology is developed transport the benefits into the area of modelling and simulation. Furthermore a framework using standard open source components is presented. This generalized multi level framework is designed to be used for simulator or model construction. It is guiding the user to separate cross-cutting concerns. Likewise the framework presents a embedded method how to use runtime validation techniques to validate complex simulation systems.

Big data analytics and scalable simulation modeling can be used to improve complex systems and processes. The explosive growth in the amount of available data for science and engineering in the coming years has the potential of enabling large scale, methodological decision making. For example, due to their high cost, careful planning of improvements to transportation systems is essential. For this problem, large amounts of sensor data from roads can be used for calibrating traffic models via optimization. In this paper, we examine the problem of traffic light synchronization in an effort to improve the efficiency of road systems.

The recent world record PHOLD performance suggests optimistic parallel discrete event simulators (OPDES) should be able to deliver superior performance at extreme scale in many application domains. In fact, programming for OPDES is extremely hard because of the necessity to write reversing and commit methods, in addition to the normal forward method of a conservative implementation. A second issue in extreme scale simulation is dealing with load imbalance. In this paper we will describe our approach to addressing these issues, using: source-to-source compiler tools to create optimistic forward, reverse and commit methods solely from the conservative method implementation; the ROSS OPDES simulator; and the Charm++ run time platform for dynamic load balancing.

Systems integration and coordination are becoming increasingly important in healthcare systems around the world, particularly in the U.S. with the new healthcare mandate. Nowhere are these strategies more important for improving patient outcomes, increasing access, and reducing costs than in outpatient surgery. Surgeries involve surgeons, anesthesiologists, nurses and perhaps other providers depending on patients’ needs. Historically, these services have been fragmented with minimal coordination amongst providers. In this paper, we briefly describe the results of a simulation project to improve outpatient surgery at the University of Texas Health Science Center in San Antonio, TX.

Projects in mechanized tunneling frequently do not reach their targeted performance. Reasons are often related to an undersized or disturbed supply-chain management of the surface jobsite. Due to the sensitive interaction of production and logistic processes, planning and analyzing the supply-chain is a challenging task. Transparent evaluation of chosen logistic strategies or project setups can be achieved by application of process simulation. This paper presents the continued work of a simulation approach to analyze the complex system of mechanized tunneling with special focus on the internal logistic as a part of the jobsite supply-chain. The generic implementation allows a flexible configuration of jobsite elements to compare possible setups. A case study demonstrates the approach and highlights the sensitive interaction of production and logistic processes under the influence of disturbances. Additionally, improvements to the original setup of the case study’s construction equipment can be derived.

This paper describes a logistic study about an underground gold mine, belonging to AngloGold Ashanti, where four different layout options could be applied to the tunnels, and also different transportation strategies. Each evaluated layout had its own configuration for shaft and truck fleet. The study was made individually for each year of the mine operation life, determining the necessary transportation capacity to achieve the planned production at that year. Due to the very restrictive traffic options in the tunnels, a framework was developed to represent the tunnels and traffic rules in a discrete-event simulation model. The results pointed the scenario with the lowest necessary transportation capacity to achieve the planned production.

We propose an agent-based model to capture the transmission patterns of diseases caused by bioterrorism attacks or epidemic outbreaks and to quickly differentiate between these two scenarios. Focusing on a region of three cities, we want to detect a bioterrorism attack when only a small proportion of the population is infected. Our results indicate that the aggregated infection and death curves in the region can serve as indicators in distinguishing between the two disease scenarios: the slope of the epidemic infection curve will increase initially and decrease afterwards, whereas the slope of the bioterrorism infection curve will strictly decrease. We also conclude that for a bioterrorism outbreak, the dynamic curve from the bioterrorism source city becomes more dominant as the local working probability increases. In contrast, the behavior of individual cities for the epidemic model presents a “time-lag” pattern. As time progresses, the individual city’s dynamic curves converge.

Individuals often use information from broadcast news (e.g. media) and narrowcast news (e.g. personal social network) to form their perception on a certain social issue. Using a case study in social risk amplification, this paper demonstrates that simulation modelling, specifically agent-based simulation, can be useful in analysing the effect of broadcast and narrowcast processes on the formation of public risk perception. The first part of this paper explains the structure of a model that allows easy configuration for testing various behaviours about which the empirical literature cannot make definitive predictions. The second part of this paper discusses the effect of personal social network and the role of media in the dynamics of public risk perception. The results show the undesirable effect of the extreme narrowcast process in society and a media that simply broadcasts the average public risk perception.

Agent-based simulations are indisputably effective for analyzing complex processes such as traffic patterns and social systems. However, human experts often face the challenges in repeating the simulation many times when evaluating a large variety of scenarios. To reduce the computational burden, we propose an approach for inferring the end results in the middle of simulations. For each simulated scenario, we design a feature that compactly aggregates the agents’ states over time. Given a sufficient number of such features we show how to accurately predict the end results without fully performing the simulations. Our experiments with traffic simulations confirmed that our approach achieved better accuracies than existing simulation metamodeling approaches that only use the inputs and outputs of the simulations. Our results imply that one can quickly evaluate all scenarios by performing full simulations on only a fraction of them, and partial simulations on the rest.

There is a boundary separating analytic methodology and simulation methodology. If a problem involves the flipping of coins or the rolling of dice, for example, analytic methods are generally employed. If a problem involves a complex series of queues with a nonstationary arrival stream, discrete-event simulation methods are generally employed. This tutorial considers problems that are near the boundary between analytic methods and simulation methods. We use the symbolic-based language APPL (A Probability Programming Language) to perform operations on random variables to address these problems. The problems considered are the infinite bootstrap, the probability distribution of the Kolmogorov-Smirnov test statistic, the distribution of the time to complete a stochastic activity network, finding a lower bound on system reliability, Benford's law, finding the probability distribution and variance-covariance matrix of sojourn times in a queueing model, probability distribution relationships, testing random numbers, bivariate transformations, and time series models.

This paper describes the implementation of an indoor positioning architecture based on radio frequency profiling using received signal strength (RSS) measurements for localizing and tracking resources in construction-related applications. The profiling-based approach is coupled with commonly used noise filtering algorithms in order to cope with the application of material tracking in a pipe spool fabrication shop. With 95% likelihood, consistent positioning accuracy of 1-2 meters away from the actual position of a tracked tag can be obtained in the fabrication shop−which is deemed sufficient for materials and labor hours tracking in support of shop production control. In particular, through simulation experiments using data collected from a pipe fabrication shop we investigated the sensitivity of the resulting localization accuracy with respect to the quantity and layout of the reference points, aimed at streamlining system updating and simplifying solution implementation.

Conventionally, efforts are made to optimize the performance of simulation models by examining several possible resource combinations. However, the number of possible resource assignments increases exponentially with the increase of the range of available resources. Many researchers combined Genetic Algorithms (GAs) and other optimization techniques with simulation models to reach the Pareto solutions. However, due to the large number of resources required in complex and large-scale construction projects, which results in a very large search space, and the lack of the GA capability in fast convergence to the optimum results, parallel computing is required to reduce the computational time. This paper proposes the usage of Non-dominated Sorting Genetic Algorithm (NSGA-) as the optimization engine integrated with Discrete Event Simulation (DES) to model the bridge construction processes. The parallel computing platform is applied to reduce the computation time necessary to deal with multiple objective functions and the large search space.

This paper proposes and tests an integrated framework for bottom-up simulation of performance in construction projects. The proposed framework conceptualizes construction projects as systems-of-systems in which the abstraction and micro-simulation of dynamic behaviors are investigated at the base-level consisting of the following elements: human agents, information, and resources. The application of the proposed framework is demonstrated in a numerical example related to a tunneling project. The findings highlight the capability of the proposed framework in providing an integrated approach for bottom-up simulation of performance in construction projects.

We simulationists are in the meta business. The best of us think in abstracted terms about the questions we are tasked to answer and the referents we are challenged to represent. Yet what we have found most elusive is the meta-language for our own identity. We have been called "tool builders" but we know we are more. Our position descriptions are entitled "engineer, scientist, programmer, analyst, researcher" but we know those words are inadequate. Our terminology overlaps with the ontology owned by the systems we simulate. Our attempts to be officially recognized as a unique profession have thus far failed. But we recognize something in ourselves and in each other that we have thus far failed to name. Literal attempts to differentiate us from other technical professionals have proven uninspiring. Is it possible for us to apply our metaphorical skills to unite our thinking and equip us with a common vision? Or are we simply very like simulationists?

Material flow forecast based on Short-Term Simulation has been established as a decision support solution for fine-tuning of Preventive Maintenance (PM) timing at Infineon Dresden. To ensure stable forecast quality for effective PM decision making, the typical tool uptime behavior needs to be portrayed accurately. In this paper, we present a hybrid tool down modeling approach that selectively combines deterministic and random down time modeling based on historical tool uptime behavior. The method allowed to approximate the daily uptime of reality in simulation. A generic framework to model historical down behavior of any distribution type, described by the two parameters Mean Time to Failure (MTTF) and Mean Time to Repair (MTTR) is also discussed.

This paper presents an approach for scheduling different types of preventive maintenances (PMs) for a work center of a semiconductor manufacturing facility. The PM scheduling problem includes time-dependent synchronization constraints and is implemented in a constraint programming model. A mix of periodic and workload-specific maintenances is scheduled considering the synchronization to available engineers which have individual shift schedules and skills that define the range feasible maintenances. This also comprises maintenances having process durations covering multiple shifts, which requires a continuous availability of sufficiently skilled engineers. Additionally to the PMs, also handling and maintaining of unscheduled downs is considered in the model. Multiple objectives are investigated and used for optimization and tested on realistic data.

In semiconductor manufacturing, preventive maintenance (PM) activities are typically scheduled via a two tier hierarchical decomposition approach. The first decision tier determines a PM cycle plan while the second tier schedules these planned events into the manufacturing operations. Following recent work based on the use of G/G/m queueing approximations for PM planning, we develop a method to allow for multiple PM cycles in a tool set. We formulate a nonlinear program with the PM cycle durations as continuous decision variables with the objective of minimizing the mean cycle time. We examine certain special cases and characterize the optimal solutions. Numerical studies are conducted with realistic multiple PM cycle data to assess the implications of the proposed approach. The results suggest that it may be possible to obtain significant improvements in the overall cycle time performance of tool sets in semiconductor manufacturing relative to existing PM planning procedures.

In 2010, over 200,000 women in the United States were diagnosed with invasive breast cancer, and an estimated 17% of those women died from the disease, according to the Centers for Disease Control and Prevention (CDC). Also in 2010, the CDC reported that 12.6 million women have diabetes, the seventh leading cause of death in the U.S. Although we know much about these prevalent diseases individually, little research has been conducted regarding the interaction between breast cancer and diabetes. In this study, we build a simulation model framework that explores this complex relationship, with an initial goal of assessing the prognosis for women who are diagnosed with diabetes considering their breast cancer risk. Using data from national survey and surveillance studies, we characterize morbidity and mortality. This framework would potentially allow us to study a variety of diseases that are comorbid to breast cancer.

We design and develop a predictive model that estimates health and economic outcomes associated with smoking cessation interventions using discrete-event simulation (DES). Outcomes include estimates of sustained abstinence from smoking, quality of life years gained, cost of treatment, additional health-related morbidity due to long-term effects of smoking (e.g. lung cancer, stroke), and cost-effectiveness of the various smoking cessation options. Interventions assessed include nicotine replacement therapy (patch or gum), oral medication (bupropion and varenicline), and abstinence without pharmacologic assistance. The DES approach allows us to account for heterogeneity of patients and dynamic changes in disease progression. Results show that even a single quit attempt can be cost-effective over the patients’ lifetime. Furthermore, based on the incremental cost-effectiveness ratios, varenicline dominates other treatments at 10 years, 30 years, and over the lifetime. Understanding the comparative effectiveness and cost of alternative smoking cessation strategies can improve clinical and patient decision-making.

Cesarean delivery is the most common major abdominal surgery in many parts of the world. As of October 2012, the cesarean section rate in the United States was reported to be 32.8% in 2011, rising from 4.5% in 1970. Cesarean sections are associated with an increased risk of neonatal respiratory morbidity, increased risk of a hysterectomy and can cause major complications in subsequent pregnancies, such as uterine rupture. To evaluate the current cesarean delivery rate due to a “failure to progress” diagnosis, our goal was to replicate the delivery process for women undergoing a trial of labor. In this simulation we evaluate the Friedman Curve and other labor progression rules to identify circumstances in which the cesarean rate can be decreased through the analysis of the total length of time a woman spends in labor as well as the duration of time a woman remains in a cervical dilation stage.

The computational study of complex systems increasingly requires model integration. The drivers include a growing interest in leveraging accepted legacy models, an intensifying pressure to reduce development costs by reusing models, and expanding user requirements that are best met by combining different modeling methods. There have been many published successes including supporting theory, conceptual frameworks, software tools, and case studies. Nonetheless, on an empirical basis, the published work suggests that correctly specifying model integration strategies remains challenging. This naturally raises a question that has not yet been answered in the literature, namely 'what is the computational difficulty of model integration?' This paper’s contribution is to address this question with a time and space complexity analysis that concludes that deep model integration with proven correctness is both NP-complete and PSPACE-complete and that reducing this complexity requires sacrificing correctness proofs in favor of guidance from both subject matter experts and modeling specialists.

This paper is the first from a series of papers that aim to develop a theory of multi-method M&S approach. The aim of this paper is to develop ontological basis for multi-method M&S approach. The first part of this paper discusses terms related to the use of more than a single modeling & simulation (M&S) method. This is to show the ontological ambiguity currently present within the M&S field in the context of using more than a single method. Next section provides the philosophical stance of the authors about the main terms in order to provide clarification and context of the term multi-method M&S approach. The last section takes these previous concepts and proposes a set of definitions relevant to a multi-method M&S approach, including its parent and derivative terms.

A simulation study consists of several well-defined stages, e.g., problem formulation, model implementation and experimentation. The application of multiple techniques in the model implementation stage is referred to as hybrid simulation, which we distinguish in this paper from a hybrid M&S study, the latter referring to studies that apply methods and techniques from disciplines like Operations Research, Systems Engineering and Computer Science to one or more stages of a simulation study. We focus on the first stage of a simulation study (and by extension a hybrid M&S study), viz., eliciting the system requirements, and conduct a review of literature in Soft Systems Methodology for healthcare operations management. We discuss the potential for the use of Qualitative System Dynamics as an additional soft OR method, complementing (rather than supplanting) existing approaches, which can further aid the understanding of the system in the problem formulation/conceptual modelling stage of a Hybrid M&S study.

Inter-cell interference (ICI) is considered as the most critical bottleneck to ubiquitous 4th generation cellular access in the mobile long term evolution (LTE). To address the problem, several solutions are under evaluation as part of LTE-Advanced (LTE-A), the most promising one being coordinated multi-point joint transmission (CoMP JT). Field tests are generally considered impractical and costly for CoMP JT, therefore the need to provide a comprehensive and high-fidelity computer model to understand the impact of different design attributes and the applicability use cases. This paper presents a novel approach to the modelling and simulation of an LTE-A system with CoMP JT by means of discrete event simulation (DES) using OPNET modeler. Simulation results are provided for a full buffer traffic model and varying the characteristics of the interface between cooperating points. Gains of up to 120% are achieved for the system throughput when using CoMP JT.

In this work, we applied modeling and simulation to plan and evaluate the capacity of a real enterprise voice gateway system. We modeled the analyzed voice gateway and assessed it under different workload scenarios. We evaluated the actual setup under the real current workload demand, as well as future expected demands in terms of voice long-distance calls using PSTN and VoIP providers. Also, the existing voice gateway capacity was tested facing calls to mobile phones through GSM Sim cards. Finally, we tested a proposal setup to reduce the number of simultaneous E1 channels used per voice call, and found that our approach reduced the E1 usage rate by 14%.

Vehicular networks are envisioned to play an important role in the building of intelligent transportation systems. However, the dangers of the wireless transmission of potentially exploitable information such as detailed locations are often overlooked or only inadequately addressed in field operational tests or standardization efforts. One of the main reasons for this is that the concept of privacy is difficult to quantify. While vehicular network algorithms are usually evaluated by means of simulation, it is a non-trivial task to assess the performance of a privacy protection mechanism.

In this paper we discuss the principles, challenges, and necessary steps in terms of privacy assessment in vehicular networks. We identify useful and practical metrics that allow the comparison and evaluation of privacy protection algorithms. We present a systematic literature review that sheds light on the current state of the art and give recommendations for future research directions in the field.

High Performance Computing is becoming an instrument in its own right. The largest simulations per¬for¬med on our supercomputers are now approaching petabytes, and at Exascale everything becomes a Big Data problem. As the volume of these simulations is growing, it is becoming harder to access, analyze and visualize these data. At the same time for a broad community buy-in we need to provide public access to the simulation results. This is only possible if the analyses and visualizations are co-located with the data. We discuss the concept of open numerical laboratories providing a public, interactive access to petascale simulations for the broad science community.

Development of an effective data analytics application for manufacturing requires testing with large sets of data. It is usually difficult for application developers to find access to real manufacturing data streams for testing new data analytics applications. Virtual factories can be developed to generate the data for selected measures in formats matching those of real factories. The vision of a virtual factory has been around for more than a couple decades. Advances in technologies for computation, communication, and integration and in associated standards have made the vision of a virtual factory within reach now. This paper discusses requirements for a virtual factory to meet the needs of manufacturing data analytics applications. A framework for the virtual factory is proposed that leverages current technology and standards to help identify the developments needed for the realization of virtual factories.

This paper investigates the impact of the dependence parameter uncertainty on data-driven decision making. When the dependence parameters are known, ignoring the dependencies in system inputs wastes significant resources in production and service systems. In the case of unknown dependence parameters, the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in certain cases. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context, propose two new finite-sample hypothesis tests to investigate the existence of this correlation, and illustrate them with examples.

Modeling is a creative activity known and practiced by most in industry, government, and academia. A model is a construct of language coded using different technologies—from print and physical media to computer-generated synthetic environments. The activity of modeling is so central to human cognition, that we must ask ourselves whether modeling should be more clearly emphasized in education, especially within science, technology, engineering, and mathematics (STEM) fields. Coding has recently been suggested as a skill that everyone needs to acquire for the 21st Century. Can modeling be situated along side coding? We explore the connections between modeling and coding, and stress the importance of model literacy for all.

We consider a one-dimensional bisection method for finding the zero of a monotonic function, where function evaluations can be performed asynchronously in a parallel computing environment. Using dynamic programming, we characterize the Bayes-optimal policy for sequentially choosing points at which to query the function. In choosing these points, we face a trade-off between aggressively reducing the search space in the short term, and maintaining a desirable spread of queries in the long-term. Our results provide insight on how this trade-off is affected by function evaluation times, risk preferences, and computational budget.

The simulation of alternative evaluations in the ranking and selection problems often requires extensive amounts of computing power, so it is natural to use clusters with several workers for this task. We propose to extend the standard Knowledge Gradient policy to allow parallel and asynchronous dispatch of computation tasks among workers and denote it as the Asynchronous Knowledge Gradient. Simulation experiments indicate that performance loss due to parallelization of computations is below 25%. This implies that the proposed policy can yield significant benefits in terms of the time needed to obtain a desired approximation of the solution. We describe a master-slave architecture allowing for asynchronous dispatching of jobs among workers that handles problems with worker failures that are encountered in cluster environments. As a test bed of the procedure we developed an emulator of a heterogeneous computing cluster that allows testing of the parallel performance of stochastic optimization algorithms.

Distributed simulations require partitioning mechanisms to operate, and the best partitioning algorithms try to load-balance the partitions. Dynamic load-balancing, i.e., re-partitioning simulation environments at run-time, becomes essential when the load in the partitions change. In decentralised distributed simulation the information needed to dynamically load-balance seems difficult to collect and to our knowledge, all solutions apply a local dynamic load balancing: partitions exchange load only with their neighbours (more loaded partitions to less loaded ones). This limits the effect of the load-balancing. In this paper, we present a global dynamic load-balancing of decentralised distributed simulations. Our algorithm collects information in a decentralised fashion and makes re-balancing decisions based on the load processed by every logical processes. While our algorithm has similar results to others in most cases, we show an improvement of the load-balancing up to 30% in some challenging scenarios against only 12.5% for a local dynamic load-balancing.

Lean Methods and Theory of Constraints (TOC) are used in the Printing and Personal Systems group at HP to drive productivity improvements in existing and planned production lines through facilitated workshop events. Lean concepts are introduced to manufacturing teams, and are used to identify opportunities to increase efficiency. The teams are also educated on TOC as a method for managing the production line. Discrete Event Simulation (using WITNESS software) was added in order to increase the effectiveness of developing improvement plans during these workshop events. This presentation will discuss the added benefit of using DES analysis to refine and drive improvement activities, resulting in faster learning cycles, increased manufacturing output and ultimately increased revenue.

The baggage handling system of the Santiago International Airport follows a semiautomatic scheme. Nowadays, it has a major problem due to high demand peaks that exceed the design capacity. Also, there is lack of communication and coordination between the agents involved, which leads to a poor visibility of each agent operation’s impact in the performance of others. In order to get an holistic view of the baggage handling associated with departures, a simulation platform was constructed to emulate the flow of bags through the system from the moment a passenger arrives and joins the check-in’s queue, until his bags are safety stored in the airplane’s cargo hold. Later the simulator was used to quantify the impact of improper handling of bags in the counters sector. We predicted that reducing the handling errors in counters would lead to a 60% reduction of bags arriving after the flight was closed.

Financial institutions receive Court Orders, Levies and Information Subpoenas from federal and state agencies inquiring about and potentially affecting customer accounts. Due to high daily variability in volumes, multiple input channels, document complexity and a variety of processing requirements, JPMC developed a production simulator and dashboard reporting system to predict and monitor document processing performance and ultimately improve service levels while reducing costs. In today’s virtual processing world, BPM and document management systems are mandatory to process online documents and provide accountability for all processes and documents. While these systems are adept at processing transactions and rules-based flow, complementary analytics are often needed to manage workflow with respect to resource constraints, costs and service level objectives. This paper describes how simulation can be used to develop a daily production plan and a series of process specific analytics to help direct the workforce to attain higher service levels at lower costs.

Self-adaptive discrete-event simulation is a general paradigm for time integration of discretized partial differential equations and particle models. This novel approach enables local time steps for equations describing time evolution of grid-based elements (fluids, fields) and macro-particles on arbitrary grids while preserving underlying conservation laws. The solution-adaptive integration ensures robustness (stability) and efficiency (speed) of complex nonlinear simulations. Using this technique we achieved a breakthrough in simulations of multiscale plasma systems. A new particle-in-cell simulation tool, HYPERS (Hybrid Particle Event-Resolved Simulator), which solves a set of strongly coupled Maxwell’s equations, electron fluid equations and ion particle equations of motion, is presented as the first multi-dimensional application of this technology. We discuss its parallel implementation and demonstrate first results from three-dimensional simulations of compact plasma objects that have been out of reach of conventional codes. Potential applications of the new methodology to other scientific and engineering domains are also discussed.

We describe, in this paper, our progress in developing a simulation environment to be used for the detailed simulation of chemical reactions and the diffusion of ions through a neuronal membrane. Neuron Time Warp (NTW) is part of the Neuron project (www.neuron.yale.edu) and is intended to be made use of by experimental neuroscientists. It relies upon the Next Subvolume Method, a stochastic Monte Carlo algorithm used to simulate chemical reactions within the membrane of a neuron. We make use of a model of a dendrite branch on which to evaluate NTW's performance. We evaluated the performance of NTW using MPI and shared memory models on a multi-core machine as a first step towards the development of a combined differential equation/ discrete event model of a neuron.

In teaching simulation, the Buffon’s needle is a popular experiment to use for designing a Monte Carlo simulation to approximate the number π. Simulating the Buffon’s needle experiment is a perfect example for demonstrating the beauty of a Monte Carlo simulation in a classroom. However, there is a common misconception concerning the Buffon’s needle simulation. Erroneously, the simulation of the needle drop cannot be used to evaluate π. We have to simulate the needle’s angle from an uniform (0,π/2) distribution. It is self-referential in theory, since it requires the number π as the input value to approximate π. In this study, we propose a new method using the fixed-point iteration to remove the inherent paradox of the Buffon’s needle simulation. A new algorithm with Python implementation is proposed. The simulation outputs indicate that our new method is as good as if we use the true π value as an input.

Many business decisions can be ably supported by applications of various simulation types, including system dynamics, discrete event, agent based, and Monte Carlo. Many decisions involving large capital investments are made only after the proposed systems have been simulated and the expected return on investment verified using simulation. The applicability of simulation to business decisions would suggest that leading business schools would include teaching of simulation software in their curriculum. This paper reports on a survey of teaching of simulation software at leading business school. The prevalence teaching some simulation types over others and the reasons provided are discussed. Simulation community may want to consider the trends at business schools and the need to influence them.

A hierarchical modeling and simulation (M&S) framework can help federal agencies integrate the myriad business resourcing decisions they face as unmanned aerospace vehicle (UAV) systems are deployed within their federally authorized charters. An integrated M&S method offers a pragmatic approach to leveraging the power of analytical techniques and coping with the complex support requirements of modern macrosystems. In this paper, we demonstrate the benefits of incorporating several agent-based modeling (ABM) enhancements for UAV route planning into a hierarchical M&S structure.

Forest fires are time evolving disasters that consume environmental and financial resources, endangering the rescue units that try to mitigate them. As such, a simulator that can predict the fire propagation is essential to locate control systems, in order to reduce the loss of natural resources without risking the firefighters. This paper proposes a simulator based on discrete representation of the selected areas where the velocity of fire propagation between neighbors depends on variables associated with locative or climatological characteristics. We consider discrete classification based on the spread in each direction. To validate our simulator we considered two scenarios: a theoretical area to test the algorithm considering complete information characteristics and a forest area near Bogota, Colombia. The results show realistic propagation patterns compared to region real past forest fire events. For a better prediction we need more reliable data and relate the fire to both location and weather characteristics.

In a steel fabrication shop, jobs from different clients and projects are generally processed simultaneously in order to streamline processes, improve resource utilization, and achieve cost-effectiveness in serving multiple concurrent steel-erection sites. Reliable quantity takeoff on each job and accurate estimate of shop fabrication man-hour requirements are crucial to plan and control fabrication operations and resource allocation on the shop floor. Building information modeling (BIM), is intended to integrate multifaceted characteristics of a building facility, but finds its application in structural steel fabrication largely limited to design and drafting. This research focuses on extending BIM’s usage further to the planning and control phases in steel fabrication. Using data extracted from BIM-based models, a linear regression model is developed to provide the man-hour requirement estimate for a particular job. Actual data collected from a steel fabrication company was used to train and validate the model.

A simulation-based heuristic is presented for a resource investment problem (RIP). This version of the RIP considers the trade-off between the number of resources, project makespan and resource utilization. A “win-win” goal is a reduction in project makespan while improving resource utilization. The RIP heuristic is presented as an executive for RCAN, a simulation tool that produces solutions to the multi-mode resource constrained project scheduling problem (RCPSP). The RIP heuristic uses feedback from RCAN to iteratively modify a set of renewable resources. The heuristic is shown to be effective on real-world, large-scale depot maintenance projects. In addition, the simulation tool uses a priority rule approach to schedule project tasks for the RCPSP problem. Therefore the RCPSP priority rule has a major effect on the RIP heuristic. An analysis is presented showing how various priority rules impact the RIP heuristic’s ability to reduce the makespan while maintaining or increasing resource utilization.

Customized planning, engineering and build-up of factory plants are very complex tasks, where project management contains lots of risks and uncertainties. Existing simulation techniques could help massively to evaluate these uncertainties and achieve improved and at least more robust plans during project management, but are typically not applied in industry, especially at SMEs (small and medium-sized enterprises). This paper presents some results of the joint research project simject of the Universities of Paderborn and Kassel, which aims at the development of a demonstrator for a simulation-based and logistic-integrated project planning and scheduling. Based on the researched state-of-the-art, requirements and a planning process are derived and described, as well as a draft of the current technical infrastructure of the intended modular prototype. First plug-ins for project simulation and multi-project optimization are implemented and already show possible benefits for the project management process.

We propose a data structure that stores previously observed vehicle paths in a given area in order to predict the forward trajectory of an observed vehicle at any stage. Incomplete vehicle trajectories are conditioned against in a Past Tree, to predict future trajectories in another tree structure - a Future Tree. Many use cases in transportation simulation benefit from higher validity by considering historical paths in determining how to route vehicle entities. Instead of assigning static and independent turn probabilities at intersections, the storage and retrieval of historical path information can give a more accurate picture of future traffic trends and enhance the capabilities of real-time simulations to, say, inform mobile phone users of expected traffic jams along certain segments, direct the search efforts of law enforcement personnel, or allow more effective synchronization of traffic signals.

In the context of a city-scale symbiotic traffic simulation, real-time data about the location of many vehicles are obtained in the form of a continuous data-stream. In this paper, we present a scalable solution for performing map-matching using sliding-windows over a GPS data-stream onto a digital road network for initializing the what-if analysis process involved in symbiotic simulations. We focus on the optimizations performed to ensure that the latency associated with the map-matching process is low while maintaining a high degree of accuracy. Experimental results reveal the range in terms of sampling interval and noise for acceptable reliability and latency.

The goal of this paper is to study the team formation of multiple UAVs and UGVs for collaborative surveillance and crowd control under uncertain scenarios (e.g., crowd splitting). A comprehensive and coherent dynamic data driven adaptive multi-scale simulation (DDDAMS) framework is adopted, with the focus on simulation-based planning and control strategies related to the surveillance problem considered in this paper. To enable the team formation of multiple UAVs and UGVs, a two stage approach involving 1) crowd clustering and 2) UAV/UGV team assignment is proposed during the system operations by considering the geometry of the crowd clusters and solving a multi-objective optimization problem. For the experiment, an integrated testbed has been developed based on agent-based hardware-in-the-loop simulation involving seamless communications among simulated and real vehicles. Preliminary results indicate the effectiveness and efficiency of the proposed approach for the team formation of multiple UAVs and UGVs.

Worldwide, and in particular in Germany, a trend toward a more sustainable electric energy supply including energy efficiency and climate protection can be observed. Simulation models can support these energy transitions by providing beneficial insights for the development of different electricity generation mix strategies in future electric energy systems. One important input parameter for these large-scale simulations is the electricity demand, commonly obtained using empirical datasets. However, it is desirable to deploy dynamic electricity demand models to be able to investigate the behavior of the energy system under changing or specific conditions. In this paper we present such a model. We identify the most important parameters, such as the seasonality, the type of day, and the daily mean temperature to accurately model the large-scale electricity demand for Germany. We validate and implement our model in the context of a hybrid simulation framework and show its correctness and applicability.

New challenges demand that manufacturing companies adopt sustainable approaches and succeed in this adoption. Energy efficiency plays a key role in achieving sustainability goals, and performance indicators are necessary beyond measurement of data to evaluate energy efficiency . In this landscape, scalable and easy-to-understand metrics providing an energy competitiveness degree of manufacturing resources are currently missing. The study aims to test through simulation applicability and potential offered by a novel Energy Overall Equipment Effectiveness – Energy OEE - indicator for discrete manufacturing firms. A simulation of a discrete manufacturing CNC machine case is used to evaluate the applicability of using Energy OEE assessment for management decision support. As a result, this study paves the way to a better exploitation of data that energy monitoring and sensor technology aim to offer in the future.

Buildings are one of the major energy consumers because of the need to meet occupants requirements. The commercial/institutional sector accounted for 14% of total energy consumption in Canada in 2009 while office buildings consumed 35% of this amount. Auxiliary equipment used 19% of the total energy consumed in office buildings. Previous studies showed the impact of occupancy behavior on IT equipment energy consumption. This paper proposes a new method for monitoring occupant behavior and energy consumption of IT equipment. Analyzing the resulting data can help evaluating the occupancy behavior impact on energy saving. Two wireless sensor technologies are investigated to collect the required data and to build an occupancy behavior estimation profile: Ultra-Wideband Real-Time Location System for occupancy location monitoring and Zigbee wireless energy meters for monitoring the energy consumption of IT equipment. The occupancy behavior estimation profile can be used to reduce energy consumption based on real-time occupants’ information.

We present a multiple objective simulation optimization algorithm called multiple objective probabilistic branch and bound (MOPBnB) with the goal of approximating the efficient frontier and the associated Pareto optimal set in the solution space. MOPBnB is developed for both deterministic and noisy problems with mixed continuous and discrete variables. When the algorithm terminates, it provides a set of non-dominated solutions that approximates the Pareto optimal set and the associated objective function estimates that approximate the efficient frontier. The quality of the solutions is statistically analyzed using a measure of distance between solutions to the true efficient frontier. We also present numerical experiments with benchmark functions to visualize the algorithm and its performance.

Engineering design optimization often involves computationally expensive time consuming simulations. Although surrogate-based optimization has been used to alleviate the problem to some extent, surrogate models (like Kriging) struggle as the dimensionality of the problem increases to medium-scale. The enormity of the design space in higher dimensions (above ten) makes the search for optima challenging and time consuming. This paper proposes the use of probabilistic support vector machine classifiers to reduce the search space for optimization. The proposed technique transforms the optimization problem into a binary classification problem to differentiate between feasible (likely containing the optima) and infeasible (not likely containing the optima) regions. A model-driven sampling scheme selects batches of probably-feasible samples while reducing the search space. The result is a reduced subspace within which existing optimization algorithms can be used to find the optima. The technique is validated on analytical benchmark problems.

Simulation models of different fidelity levels are often available for a complex system. High-fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are faster and can evaluate a large number of solutions. But their results may contain significant bias and variability. We propose an Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to exploit the benefits of high- and low-fidelity simulations to efficiently identify a (near) optimal solution. MO2TOS uses low-fidelity simulations for all solutions and then assigns a fixed budget of high-fidelity simulations to solutions based on low-fidelity simulation results. We show the benefits of MO2TOS via theoretical analysis and numerical experiments with deterministic simulations and stochastic simulations where noise is negligible with sufficient replications. We compare MO2TOS to Equal Allocation (EA) and Optimal Computing Budget Allocation (OCBA). MO2TOS consistently outperforms both EA and OCBA.

The research direction in modeling complex, chronic conditions for health policy evaluation has been to incorporate individual heterogeneity. This detail makes our models more powerful and relevant. In implementing an individual simulation model of colorectal cancer we have recognized two considerations related to incorporating individual heterogeneity that have not been adequately discussed in the literature. First, there are substantial computational gains and interpretation benefits if an individual’s life courses are identical except when differences are directly induced by an intervention. Achieving this is not trivial. We have developed the notion of a “common patient” who is the same between scenarios except for intervention-induced changes. We create the common patient using a careful application of Common Random Numbers (CRN). Second, when we model differentiated individuals we can examine differing impacts of polices on specific sub-groups. This leverages the detail in individual attributes to produce useful results for policy makers.

Type2 Diabetes Mellitus (T2DM) and its complications account for 11% of the global health expenditure (IDF 2012). Different primary, secondary, and tertiary preventive interventions promise better health outcomes and cost savings but are often studied separately. This paper proposes a simulation model for T2DM that comprehends the nonlinear interactions of multiple interventions for various stages of T2DM on population dynamics, health outcomes, and costs. We summarize the model, then demonstrate how we addressed the important challenge of fitting input parameters given that data needed to be combined from disparate sources of data sources in a way that calibrates input parameters to output metrics over a range of decision variables (a form of model calibration to achieve a response model match to clinical data). We present preliminary numerical results to inform policies for T2DM prevention and management.

Influenza is a serious public health concern and vaccination is the first line of defense. In a pandemic, individuals are prioritized based on their risk profiles and transmission rates to ensure effective use of available vaccine. We use an agent-based stochastic simulation model, and optimize the age-specific vaccine distribution strategy. We use numerical optimization techniques to minimize the overall cost of the outbreak. Our computational experiments show that the best policy returned by our approach outperforms alternative policies recommended by Centers for Disease Control and Prevention.

When we use simulation to estimate the performance of a stochastic system, lack of fidelity in the random input models can lead to poor system performance estimates. Since the components of many complex systems could be dependent, we want to build input models that faithfully capture such key properties. In this paper, we use the flexible NORmal To Anything (NORTA) representation for dependent inputs. However, to use the NORTA representation we need to estimate the marginal distribution parameters and a correlation matrix from real-world data, introducing input uncertainty. To quantify this uncertainty, we employ the bootstrap to capture the parameter estimation error and an equation-based stochastic kriging metamodel to propagate the input uncertainty to the output mean. Asymptotic analysis provides theoretical support for our approach, while an empirical study demonstrates that it has good finite-sample performance.

For system dynamics simulation (SD) models, an estimation of statistical distributions for uncertain parameters is crucial. These distributions could be used for testing models sensitivity, quality of policies, and/or estimating confidence intervals for these parameters. Assumptions related to normality, independence and constant variation are often misapplied in dynamic simulation. Bootstrapping holds a considerable theoretical advantage when used with non-Gaussian data for estimating empirical distributions for unknown parameters. Although it is a widely acceptable approach, it has had only limited use in system dynamics applications. This paper introduces an application of Direct Residual Bootstrapping (DRBS) for statistical inference in system dynamic model. DRBS has been applied successfully to ‘The Irish Elderly Patient Delayed Discharge’ dynamic model to estimate empirical distribution for some unknown parameters with a minimal computation effort. The computational results show that bootstrapping offers an efficient performance in cases of no availability of prior information of model parameters.

In some service operations settings, data are available only for system outputs but not the constituent input models. Examples are service call centers and patient flows in clinics, where sometimes only the waiting time or the queue length data are collected for economic or operational reasons, and the data on the "input distributions", namely interarrival and service times, are limited or unavailable. In this paper, we study the problem of estimating these input distributions with only the availability of the output data, a problem usually known as the inverse problem, and we are interested in the context where stochastic simulation is required to generate the outputs. We take a nonparametric viewpoint, and formulate this inverse problem as a stochastic program by maximizing the entropy of the input distribution subject to moment matching. We then propose an iterative scheme via simulation to approximately solve the program.

Among the typical production modes, Make-To-Stock (MTS) may provide efficient service to customers but high inventory is a problem, while Make-To-Order (MTO) can improve the market response ability of enterprise but the stochastic characteristics of orders usually increase setup time and cost. The case study shown in this paper focuses on the decision making of production mode faced by DVS Company in Shenzhen of China. A discrete-event simulation model was built via ARENA and VBA to simulate the operations of MTO instead of current MTS mode. One-month real-life data were collected to validate the model and drive its run. The experimental results show that MTO is effective in cost saving and quick response for DVS company. To ensure the on-time rate of orders, it is suggested that the company should make two moulds for “large” orders and train workers to decrease setup times by simulation analysis.

To accommodate changes in demand, companies often require upgrades to their Distribution Center (DC) material handling equipment. These upgrades cost millions of dollars, and it can be difficult to understand exactly what the impact on operations will be. Savvy managers use simulation to help understand how the upgraded systems will perform, using simulation models as “insurance” that their investment will pay off.

Accurate simulation models help DC managers reduce risk, and also help them see how far into the future these upgrades will work. In this presentation, we will review two recent DC case studies, and discuss how AutoMod® simulation models provided the level of confidence the customer required before making their investment in new equipment. Both models use actual data to drive the simulations, and the clients acquired AutoMod® run time licenses to build internal support for simulation, assist in validation, and to perform continued experimentation.

A Beverage Company wants to balance its Warehouse’s incoming vs. outgoing demand, only possible if the underlying processes and bottlenecks are fully understood.

Palletized products enter the warehouse (14,016 bins, 32 rows, 8 bins high) from two plants where dedicated forklifts are used to take pallets to put away staging areas, from picked staging areas to bulk pre-pack areas and to load trucks. Very Narrow Aisle (VNA) forklifts are used for put away and picking in the bulk rack area.

Constraints include: • Forklifts cannot cross if staged pallets are more than 5 deep • Glass can only be stacked on glass • Carton cannot be stacked at all • Staging area needs empty adjacent areas

Key success parameter is VNA tasks/hour.

A Simio model with free space movement, multi-dimension arrays and 3D space pallet storage was used to gain insight into the processes.

AddSIM is a component-based simulation environment that has been developed for weapon system modeling and engagement simulation. While AddSIM addresses component-based model development and reuse issues, it does not fully consider distributed simulation for massive and frequently changing data. We studied some approaches to develop an engagement simulation environment based on data distribution service for distributed systems (DDS). This article introduces three different approaches to apply DDS to AddSIM and describes their advantages and disadvantages. Then, we choose the best way to develop the mixture of AddSIM and DDS. According to the proposed approach, we explain the data exchange mechanism between AddSIM nodes and time synchronization for correct execution. We also define several DDS topic types for interoperation. Finally, we describe an anti-ship warfare case study to show the difference between AddSIM and the proposed approach.

Interoperation of simulation models is an important issue due to high level requirements of reusability, scalability, and eventually training effect. Achieving Live, Virtual and Constructive (LVC) simulation interoperability is a main goal and a major challenge for M&S community. High interoperability quality in LVC simulation environment is a technologically complex task, being affected by multiple factors, and the task is not yet satisfactorily characterized and studied. This research presents an experimental LVC simulation framework to model and simulate complex war fighting scenarios. Our experimental framework implementation discusses key issues for LVC interoperability encountered during our experimentation. A case study is presented to discuss LVC integration and interoperability challenges. Our experimental research aim is to contribute to the definition and design concepts of LVC simulation systems developments, technological considerations and adequate interoperability.

AddSIM is a simulation environment for integrating models such as platforms, sensors, command and control systems, and shooters into the same synthetic battle field. AddSIM aimed to integrate the models which were developed and used during each weapon system development phase. AddSIM consists of four components; user interface including resource repositories, simulation engine (kernel), external interface, and support services. The user interface is a set of tools to make models, set-up scenarios, check execution options, run the kernel, and analyze the simulation results. The kernel can manage discrete-event and discrete-time hybrid simulations and run the simulation in stand-alone or distributed modes. It can also support parallel simulations. AddSIM can interoperate with legacy models in various forms, C/C++, MATLAB, HLA/RTI, and DIS, through the AddSIM external interface functions. The support services are environmental services (terrain, atmosphere, and maritime), spatial service, journaling/logging service, and utility service.

When using simulations for decision making, no matter the domain, the uncertainty of the simulations' output is an important concern. This uncertainty is traditionally estimated by propagating input uncertainties forward through the simulation model. However, this approach requires extensive data collection before the output uncertainty can be estimated. In the worst case scenario, the output may even prove too uncertain to be usable, possibly requiring multiple revisions of the data collection step. To reduce this expensive process, we propose a method for inverse uncertainty propagation using Gaussian processes. For a given bound on the output uncertainty, we estimate the input uncertainties that minimize the cost of data collection and satisfy said bound. That way, uncertainty requirements for the simulation output can be used for demand driven data acquisition. We evaluate the efficiency and accuracy of our approach with several examples.

Prior to making a multiple attribute selection decision, a decision-maker may collect information to estimate the value of each attribute for each alternative. In this work, we consider a fixed experimental sample budget and address the problem of how best to allocate this budget across three attributes when the attribute value estimates have a normally distributed measurement error. We illustrate that the allocation choice impacts the decision-maker’s ability to select the true best alternative. Through a simulation study we evaluate the performance of a common allocation approach of uniformly distributing the sample budget across the three attributes. We compare these results to the performance of several allocation rules that leverage the decision-maker’s preferences. We found that incorporating the decision-maker’s preferences into the allocation choice improves the probability of selecting the true best alternative.

Effective uncertainty evaluation is a critical step toward real-time and robust decision-making for complex systems in uncertain environments. A Multivariate Probabilistic Collocation Method (M-PCM) was developed to effectively evaluate system uncertainty. The method smartly chooses a limited number of simulations to produce a low-order mapping, which precisely predicts the mean output of the original mapping. While the M-PCM significantly reduces the number of simulations, it does not scale with the number of uncertain parameters, making it difficult to use for large-scale applications. In this paper, we develop a method to break the curse of dimensionality. The method integrates M-PCM and Orthogonal Fractional Factorial Design (OFFD) to reduce the number of simulations. The integrated M-PCM-OFFD predicts the correct mean of the original mapping, and is the most robust to numerical errors among all designs of the same number of simulations. The analysis also provides new insights on the optimality of OFFDs.

This paper provides an analysis of the impact, on the capacity, of decisions that are taken when sequencing tasks on machines in an industrial workshop. This study have been led on a particular workshop of a microelectronics plant. Such area is well-known in semi-conductor manufacturing to be very complex to schedule and subject to many constraints (external resources, sequence-dependent setup times, lot families, etc.). Hence, this paper also contains a comparison, on different heuristics, of the capacity when some of these constraints are removed.

As an important and challenging problem, the scheduling of semiconductor manufacturing is a hot topic in both engineering and academic fields. Its purpose is to satisfy production constraints on both production process and resources, as well as optimizing some performance indexes like cycle-time, movement, etc. However, due to its complexities, it is hard to describe the scheduling process with a mathematical model, or to use conventional methods to optimize its scheduling problem. A Simulation approach is proposed to optimize the scheduling of a semiconductor manufacturing system, i.e., a simulation-based optimization (SBO) approach. Because the high computational cost of SBO approach could hinder its application in the real production line, a parallel/distributed architecture is discussed to improve its efficiency. Using genetic algorithm (GA) as an optimization algorithm, the proposed parallel-SBO based scheduling approach for semiconductor manufacturing system is tested for its feasibility and effectiveness.

Developing dispatching rules for complex production systems such as semiconductor manufacturing is an involved task usually performed manually. In a tedious trial-and-error process, a human expert attempts to improve existing rules, which are evaluated using discrete-event simulation. A significant improvement in this task can be achieved by coupling a discrete-event simulator with heuristic optimization algorithms. In this paper we show that this approach is feasible for large manufacturing scenarios as well, and it is also useful to quantify the value of information for the scheduling process. Using the objective of minimizing the mean cycle time of lots, we show that rules created automatically using Genetic Programming (GP) can clearly outperform standard rules. We compare their performance to manually developed rules from the literature.

The quality of a dynamic ridesharing service is critical for commuters that usually must reach daily their end destination on time. In order to have a reasonable quality the waiting time for a ride service has to be low. This paper describes a dynamic ride sharing model proposal for commuters living in a small community in the Barcelona Metropolitan area. The proposal tries to solve the communication problems between the community and a communication hub served by train and buses. A pool has been issued to the community citizens in order to know if they are interested on the idea and willing to participate in a first test pilot. A simulation model has been built in order to decide which dynamic ride sharing model could be most appropriate given the limited numbers of answers received and the irregular mobility patterns of drivers and passengers.

Aircraft boarding is one of the critical processes affecting the turnaround time when a plane is at an airport. In this work, the aircraft boarding problem is studied with the aim of designing a boarding strategy that reduces the total boarding time considering the passengers’ behavior as well as the underlying grouping relationships among them. Our analysis suggests that some of the strategies proposed in other studies are highly theoretical and do not consider the individual characteristics of the passengers or their grouping inside the plane. This limits their applicability and validity in real operations. We propose a novel boarding strategy that is designed to reduce the total boarding time while, at the same time, aims at guaranteeing an acceptable quality of service.

Discrete event simulation (DES) techniques cover a broad collection of methods and applications that allows imitating, assessing, predicting and enhancing the behavior of large and complex real-world processes. This work introduces a modern DES framework, developed with SIMIO simulation software, to optimize both the design and operation of a complex beer packaging system. The proposed simulation model provides a 3D user-friendly graphical interface which allows evaluating the dynamic operation of the system over time. In turn, the simulation model has been used to perform a comprehensive sensitive analysis over the main process variables. In this way, several alternative scenarios have been assessed in order to achieve remarkable performance improvements. Alternative heuristics and optimization by simulation can be easily embedded into the proposed simulation environment. Numerical results generated by the DES model clearly show that production and efficiency can be significantly enhanced when the packaging line is properly set up.

While the management of a simulation project has many of the characteristics of traditional project management, it also has a number of unique issues that must be addressed. This paper will address the common, the not-so-common, and the treacherous aspects of simulation project management. A simulation specific series of steps will be presented. The paper also includes examples of past projects, the issues that arose, and how they were resolved.

Technical debt is a well understood yet understudied phenomena. A current issue is the verification and validation of proposed methods for technical debt management in the context of agile development. In practice, such evaluations are either too costly or too time consuming to be conducted using traditional empirical methods. In this paper, we describe a set of simulations based on models of the agile development process, Scrum, and the integration of technical debt management. The purpose of this study is to identify which strategy is superior and to provide empirical evidence to support existing claims. The models presented are based upon conceptual and industry models concerning defects and technical debt. The results of the simulations provide compelling evidence for current technical debt management strategies proposed in the literature that can be immediately applied by practitioners.

The demand for higher product availability has increased through product and service offerings such as Product Service Systems (PSS), where the product is sold for its use rather than the product itself. This has led to pressures on maintenance operations, particularly for out of sight products. Some authors have suggested applying sensors and the use of diagnostics and prognostics to monitor product performance driven by the generally held belief that diagnosing and/or predicting future failure will lead to higher product availability. In this paper, we show the ability of Discrete Event Simulation (DES) to compare between different product monitoring levels. This capability is then applied to an industrial case to investigate whether or not the higher the monitoring level leads to higher product availability.

Conceptual Modeling (CM) has gained a lot of interest in the recent years and it is widely agreed that CM is the most important phase of simulation study. Despite its significance, there are very few techniques that can help to develop well-structured and concise conceptual models. This paper proposes the use of the Structured Analysis and Design Technique (SADT) from software engineering to develop conceptual models. SADT has proven to be successful in the development of software systems, specifically in the requirements gathering phase. This paper contributes to the area of CM by proposing a new framework for developing conceptual models which focuses mainly on the first phase of CM, that of System Description (SD). A simple case, the Panorama Televisions production plant, is used to illustrate the application of this approach. The benefits and limitations of this framework are discussed.

Transportation of perishable products increased in volume in the last decades and imposes new challenges to today’s logistics operations. Because of the spoilage risk of the transported items, certain conditions (e.g., temperature, humidity, vibration, etc.) must be maintained during the transportation phase. Failure to maintain the required conditions may lead to product spoilage and delivery disturbances. This work considers a multi-echelon supply chain model composed of one producer and multiple customers and contrasts the performance of the logistics operations when RFID and sensing technologies are employed and when they are not. The simulation models of the two scenarios result in as much as nine different outcomes, which are presented in the form of good practice recommendations.

The paper presents some designing and organizational problems related to cross-docking terminals. These problems are defined based on literature review. The authors identify the problem of mutual arrangement of relational areas on terminal entrance and on exit as well as possible labor savings to be achieved through proper arrangement of these areas. A typical mathematical model is presented based on the literature. The paper provides also the discussion about analytical and simulation approaches to solve these problems. The authors describe how the problem of assignment areas can be solved using simulation software available on market and present a case study based on real data.

Oil refining companies and distributors often use pipelines to transport their products. In highly integrated, geographically challenging contexts, this may result in complex logistical systems. Pipelines which transport multiple products connect tanks, forming a particular, self-contained environment where distribution routes (called logistical channels), tactical inventory locations and operational criteria are defined to transfer, receive and deliver liquid oil derivatives. This paper describes a simulation model designed to represent such a regional pipeline network and includes a case study of a Brazilian region with refineries, a maritime terminal, a hub terminal and distribution bases.

The high variety of and intermittent demand for semiconductor memory products frequently limits the use of forecast error normalization in estimating inventory. Inventory turnover is a practical performance indicator that is used to calculate the number of days for which a company retains inventory before selling a product. Although previous studies on inventory level settings have primarily applied information regarding demand variability and forecast error, few studies have investigated the inventory turnover for inventory decisions. Inventory turnover data are time scaled, suited for a small sample, and right censored to fit the input of survival analysis. In this study, a model in which inventory turnover and survival analysis were integrated was developed to estimate the production inventory survival function used to determine inventory level. Data analysis results based on real settings indicated the viability of using inventory survival analysis to determine semiconductor memory inventory level settings.

In semiconductor manufacturing, early life failures have to be screened out before delivery. This is achieved by means of burn-in. With the aim to prove a target reliability level and release burn-in testing of the whole population, a burn-in study is performed, in which a large number of items is investigated for early life failures. However, from a statistical point of view, there is substantial potential for improvement with respect to the modeling of early life failure probabilities by considering further available information in addition to the performed burn-in studies. In this paper, we provide ideas on how advanced statistics can be applied to efficiently reduce the efforts of burn-in studies. These ideas involve scaling the failure probability with respect to the sizes of the different products, as well as taking advantage of synergies between different chip technologies within the estimation of the chips' failure probability level.

In this study, fatigue life of power semiconductor devices is measured in cycles to failure during an accelerated stress test in a climate chamber. The tested devices fail mainly in a short circuit event and their physical inspection reveals cracks in the power metallization. Commonly, the time till fracture of macroscopic metal layers is modeled with S-N or e-N fields, this means that the lifetime (N) depends on the mechanical stress (S) or the strain (e), respectively. Metal layers of semiconductor devices are microscopic (≤20um) and, in general, their ageing mechanisms are different than for macroscopic layers, nevertheless the application of the macroscopic based e-N model to semiconductor lifetime data shows good results. The parameter estimates are not only mathematically but also physically plausible, which indicates that fatigue life due to micro-mechanisms can be described by parameters representing the mechanical load (strain) in the device.

The expected opportunity cost is an important quality measure for the selection for the best simulated design among a set of design alternatives. It takes the case of incorrect selection into consideration and is particularly useful for risk-neutral decision makers. In this paper, we characterize the optimal selection rule which minimizes the expected opportunity cost by controlling the number of simulation replications allocated to each design. The observation noise of each design is allowed to have a general distribution. A comparison with other selection procedures in the numerical experiments shows the higher efficiency of the proposed method.

We consider the problem of finding a zero of an unknown function, for which we are only provided with noise-corrupted observations. Stochastic Approximation (SA) is the most popular method for solving such problems, but SA requires the practitioner to selectively tune arbitrary parameters to generate convergence behaviour, where the tuning is very application specific. We propose a fully sequential Monte Carlo sampling method which replaces the hard-to-tune parameters with an adaptively sampled estimator whose quality is carefully assessed at each iteration of the stochastic recursion using a relative width confidence interval on its optimality gap. Under mild conditions, the asymptotic behavior of the proposed method is investigated for various adaptive sampling rules.

Water is a key issue in sustainable urban development. SWIM (Simulating Water, Individuals and Management) is an agent-based model of water supply, management structure, and residential water consumer perception and behavior. Initial work applied data mining on newspaper articles to map networks of water management institutions and structures. SWIM extends this by linking an agent-based model of residential water consumption connected via networks of water managers to a global-scale hydrological model. In our case study, we focus on Tucson, AZ, where management and social behaviors are well documented. Census data are used to create synthetic populations of consumers endowed with price sensitivity and behaviors impacting water use. Social networks, including those based on geographic proximity, allow water use behaviors to spread to others. We examine possible factors leading to recent attested declines in per-capita water use, leveraging ensemble runs on high-performance computing resources to strategically explore complex parameter spaces.

Purchasing activities consume more than half of manufacturing and trading organizations sales capitals. Effective procurement is tied with efficient and highly accurate collection of data needed for purchasing the right material with the acceptable quality from appropriate suppliers. Supply chain management (SCM) consists of complex networks of distributed actors in which the problem of identifying the appropriate suppliers and allocating optimal order quantities based on the Triple Bottom Line (TBL) attributes is strategically important. However, implementation of an autonomous and automated assessment that can incorporate dynamics and uncertainty of the whole supply chain during the assessment period is not addressed. In the current research paper, a novel framework is designed and proposed to narrow the aforementioned gap. Agent technology has been incorporated in the developed framework to decrease the supplier chain uncertainty by decreasing human interactions and automating the process of supplier evaluation and order allocation.

The need for appropriate evacuation strategies is always a daunting problem when confronted with a large-scale natural disaster. The challenge is to find a policy that maximizes the number of survivors and minimizes the total cost simultaneously under a set of resource and geographic constraints. We develop an agent-based discrete event simulation (ABDES) evacuation framework based on an embedded geographic information system (GIS) module to solve a network evacuation problem that involves multiple candidate shelters, multi-priorities evacuees and several vehicle types. The evacuation framework consists of three interacting components: a disaster scenario generator module, a GIS module for analyzing an evacuation network, and an ABDES module. We conduct experiments using the city of Galveston as an example. The evacuation framework offers insight to decision-makers about the number and location of shelters, allocation and assignment of evacuation vehicles, and distribution of relief resources that are required to complete a large-scale evacuation.

The development of predictive tools for emergency management has recently become a subject of major consideration among emergency responders, especially at the federal level. Often the news of an impending high-consequence threat causes significant stress on these agencies because of their inability to apprise management of probable impacts with sufficient certainty. This paper documents Argonne National Laboratory’s effort to demonstrate the predictive capability of its newly enhanced tool called EPfast in estimating the impacts of postulated events on our power system. Specifically, the study focuses on EPfast’s ability to estimate power outage areas resulting from random system contingencies. The San Diego September 8, 2011, blackout that affected most of southern California was selected for simulation using EPfast. Results showed agreement with actual reported impacts in both spatial and quantitative terms. The method, assumptions, and data used are presented here, and results showing their potential application to emergency planning are discussed.

This paper considers the factor screening problem with multiple responses for simulation experiments. The objective is to identify critical factors with controlling Family-Wise Error Rate. The metamodel of interest is the first order linear model. Factors are independent with each others, and responses are from multivariate normal distribution. Two procedures, Sum Intersection Procedure (SUMIP) and Sort Intersection Procedure (SORTIP) are proposed and verified. Numerical studies are provided to demonstrate the validity and efficiency of our proposed procedures.

Often the accurate estimation of multiple values from a single simulation is of practical importance. Among the many variance reduction methods known in the literature, stratified sampling is especially useful for such a task as the allocation fractions can be used as decision variables to minimize the overall error of all estimates. Two different classes of overall error functions are proposed. The first, including the mean squared absolute and the mean squared relative error, allows for a simple closed-form solution. For the second class of error functions, including the maximal absolute and the maximal relative error, a simple and fast heuristic is proposed. The application of the new method, called “multiresponse stratified sampling”, and its performance are demonstrated with numerical examples.

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods.

Simulation-optimization has received a spectacular attention in the past decade. However, the theory still cannot meet the requirements from practice. Decision makers ask for methods solving a variety of problems with diverse aggregations and objectives. To answer these needs, the interchange of solution procedures becomes a key requirement as well as the development of (1) general modeling methodologies able to represent, extend and modify simulation-optimization as a unique problem, (2) mapping procedures between formalisms to enable the use of different tools. However, no formalism treats simulation-optimization as an integrated problem. This work aims at partially filling this gap by proposing a formalism based upon Event Relationship Graphs (ERGs) to represent the system dynamics, the problem decision variables and the constraints. The formalism can be adopted for simulation-optimization of control policies governing a queueing network. A Kanban Control System optimization is proposed to show the whole approach and its potential benefits.

We propose a Discrete Event Simulation (DES) model for an emergency department (ED). The model is developed in close collaboration with the French hospital Saint Camille, and is validated using real data. The objective of this model is to help ED managers better understand the behavior of the system and to improve the ED operations performance. The most essential features of an ED are considered in the model. A case study is conducted in order to allow decision makers select the most relevant investment in the human staffing budget. A simulation-based optimization algorithm is adopted to minimize the average Length of Stay (LOS) under a budget constraint. We conduct a sensitivity analysis on the optimal average LOS as a function of the staffing budget, and derive useful recommendations to managers on how the budget can impact the performance of the system.

This paper addresses the hospitalization admission control policies of patients from an emergency department that should be admitted shortly or transferred. When an emergency patient arrives, depending on his/her health condition, a physician may decide to hospitalize him/her in a specific department. Patient admission depends on the availability of beds, the length of stay and the reward of hospitalization which are both patient-type specific. The problem consists in determining patient admission policies in order to maximize the overall gain. We first propose a Markov Decision Process Model for determination of the optimal patient admission policy under some restrictive assumptions such as exponentially distributed length of stay. A simulation model is then built to assess MDP admission policies under realistic conditions. We show that MDP policies significantly improve the overall gain for different types of facilities.

Emergency Rooms (ER) are resource constrained systems that demand efficient management in order to fulfill their care and service objectives. This article explores the use of real-time simulation to improve daily operations at an ER where unplanned events may occur. Such a simulation model must adequately portray the current state of the system; however ER workflow management systems often provide only limited information for this purpose. This paper studies the impact of the model’s ability to predict ER performance with a limited amount of input information, such that what-if questions may be asked to guide decision making. Towards this end, two input data scenarios are compared to a case of perfect information. One scenario considers only patient arrival times and the other assumes additional knowledge of patient care pathways. Although both generate similar performance measures, the case with least information yields a slightly worse estimate of patient care pathway composition.

Multi-objective simulation optimization was performed to synergistically investigate the cost and benefits of the most commonly-used strategies for H1N1 epidemic mitigation: vaccination, antiviral treatment, and school closure. By simultaneously considering the three intervention strategies, this study leads to findings that supplement those in the existing work, and provides additional insights regarding intervention decision making. Specifically, our investigation suggests that different vaccine prioritization strategies, the age-based vs. ACIP (Advisory Committee on Immunization Practices) recommendation, be implemented depending on vaccine availability; individual school closure policies are favored over their global counterparts, at least when both vaccination and antiviral treatment are implemented with relatively plentiful medicine supply. The trade-offs of cost and benefits of the intervention strategies were investigated, and can be used to support relevant decision making.

Tuberculosis (TB) is an infectious disease that can progress rapidly after infection or enter a period of latency that can last many years before reactivation. Accurate estimation of the proportion of TB disease representing recent versus remote (long ago) transmission is critical to disease-control policymaking (e.g., high rates of recent transmission demand more aggressive diagnostics). Existing approaches to this problem through cluster analysis of TB strains in population-based studies of TB molecular epidemiology are crude and prone to bias. We propose an agent-based simulation of TB transmission in conjunction with molecular epidemiologic techniques that enables study of clustering dynamics in relation to disease incidence, diversity of circulating strains, sampling coverage, and study duration. We perform a sequence of simulation experiments with regard to different levels of each factor, and study the accuracy of estimates from the cluster-analysis method relative to the true proportion of incidence due to recent transmission.

Dengue fever represents a great challenge for many countries, and methodologies to prevent and/or control its transmission have been largely discussed by the research community. Modeling is a powerful tool to understand epidemic dynamics and to evaluate costs, benefits and effectiveness of control strategies. In order to assist decision-makers and researchers in the evaluation of different methodologies, we developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. DengueME provides a series of compartmental and individual-based models, implemented over a GIS database, that represents the Aedes aegypti’s life cycle, human demography, human mobility, urban landscape and dengue transmission. The platform is designed to allow easy simulation of intervention scenarios. A GUI was developed to facilitate model configuration and data input.

Like a system model in information systems and software engineering (IS/SE), also a system model in simulation engineering mainly consists of an information model and a process model. In the fields of IS/SE there are widely used standards such as the Class Diagrams of the Unified Modeling Language (UML) for making information models, and the Business Process Modeling Notation (BPMN) for making process models. This tutorial shows how to use UML class diagrams and BPMN process models at all three levels of model-driven simulation engineering: for making conceptual simulation models, for making platform-independent simulation design models, and for making platform-specific, executable simulation models.

In May 2013, NASA requested a study to develop a discrete event simulation (DES) model that analyzes the launch campaign process of the Space Launch System (SLS) from an integrated commodities perspective. The scope of the study includes launch countdown and scrub turnaround and focuses on four core launch commodities: hydrogen, oxygen, nitrogen, and helium. Previously, the commodities were only analyzed individually and deterministically for their launch support capability, but this study was the first to integrate them to examine the impact of their interactions on a launch campaign as well as the effects of process variability on commodity availability. The model utilized the flow process modules in Rockwell Arena to simulate the commodity flows and calculate total use. The study produced a validated DES model that showed that Kennedy Space Center’s ground systems were capable of supporting a 48-hour scrub turnaround for the SLS.

Before the introduction of discrete rate simulation (DRS), simulating mixed continuous and discrete event (hybrid) systems presented unique challenges that existing simulation methodologies were not suited or adaptable enough to solve. In the 1990’s a new simulation methodology, discrete rate simulation, was developed to address those issues. The most challenging aspect of discrete rate simulation is to accurately calculate the movement of material (“flow”) over a wide range of simulated systems. To calculate flow rates, the first generation DRS technology used an iterative algorithm based on the propagation of potential rates. The iterative approach failed to provide an accurate calculation of flow for many DRS models, limiting the use of this technology to a small range of real world problems. Discussed here is how the second generation DRS technology uses a global oversight approach to model the full range of DRS systems and resolve the issues associated with earlier methods.

Demographic microsimulation often focuses on effects of stable macro constraints on isolated individual life course decisions rather than on effects of inter-individual interaction or macro-micro links. To change this, modeling and simulation have to face various challenges. A modeling language is required allowing a compact, succinct, and declarative description of demographic multi-level models. To clarify how such a modeling language could look like and to reveal essential features, an existing demographic multi-level model, i.e., the linked life model, will be realized in three different modeling approaches, i.e., ML-DEVS, ML-Rules, and attributed pi. The pros and cons of these approaches will be discussed and further requirements for the envisioned language identified. Not only for modeling but also for experimenting languages can play an important role in facilitating the specification, generation, and reproduction of experiments, which will be illuminated by defining experiments in the experiment specification language SESSL.

Virtual worlds are general-purpose real-time simulation of three-dimensional environments, and serve for several purposes, such as physics simulation, collaboration, and entertainment. Due to the real-time nature of these simulations, scaling the number of in-world entities and interacting users is challenging. In this paper we present a novel approach to scalable virtual worlds, combining two dimensions of workload partitioning: space and operations. We present this new design as the Distributed Scene Graph with microcells (DSG-M), and evaluate our approach in a distributed physics intensive evaluation aimed at testing two hypothesis: (1) the space partitioning approach improves scalability by balancing the load of an overwhelmed physics dedicated simulator; and (2) simulation precision can be maintained by assigning read-only spaces near the partition borders. Results show evidence to confirm both hypotheses, and of successfully scaling the simulation of an overwhelming workload.

We present a GPU-based simulator engine that performs all steps of large-scale network simulations on a commodity many-core GPU. Overhead is reduced by avoiding unnecessary data transfers between graphics memory and main memory. On the example of a widely deployed peer-to-peer network, we analyze the parallelism in large-scale application-layer networks, which suggests the use of thousands of concurrent processor cores for simulation. The proposed simulator employs the vast number of parallel cores in modern GPUs to exploit the identified parallelism and enables substantial simulation speedup. The simulator adapts its configuration at runtime in order to balance parallelism and overheads to achieve high performance for a given network model and scenario. A performance evaluation for simulations of networks comprising up to one million peers demonstrates a speedup of up to 19.5 compared with an efficient sequential implementation and shows the effectiveness of the runtime adaptation to different network conditions.

One of the issues of parallelizing large-scale agent-based traffic simulations is partitioning and load-balancing. Traffic simulations are dynamic applications where the distribution of workload in the spatial domain constantly changes. Dynamic load-balancing at run-time has shown better efficiency than static partitioning in many studies. However, existing work has only focused on geographic partitioning methods which do not consider the minimization of communication overhead. In this paper, a graph-based dynamic load-balancing mechanism which minimizes the communication overhead during load-balancing operations is developed. Its efficiency is investigated in the agent-based traffic simulator SEMSim Traffic using real world traffic data. Experiment results show that it has significantly better performance than static graph partitioning methods in improving the overall speed of the simulation.

Inventory optimization is a common problem for most process and manufacturing industries. Higher inventory levels ensure sustained operation and high customer service levels but they hold up the working capital. While identifying optimal inventory levels using standard safety stock calculations is fairly straightforward, it becomes a cumbersome task for complex supply chains with multiple constraints and interactions. Eastman has applied discrete event simulation to understand one of its crucial supply chains by capturing key factors of the inter-related supply chain including demand uncertainty and uncontrollable events. The use of simulation provided an efficient heuristic solution and a platform to understand overall supply chain reliability by evaluating critical bottlenecks and testing different “What-if” scenarios.

It is difficult to use manufacturing logistics simulation applications, such as DELMIA QUEST and Siemens Plant Simulation, in shipyards. Because existing manufacturing logistics simulation applications are not developed considering shipyards, it was difficult to flexibly reflect the shipyard production environment. To solve this problem, this study created a simulation model based on the shipyard’s schedule plan and production plan.

The goal of this project is to design, develop and simulate a stochastic model representing the vehicle flow between ZEAL (External Parking lot for trucks) and Port of Valparaíso. The main parameters to consider are the Gates rate in both points, operation times, parking lot capacity, desired speed, rout availability, allow passing zones, and blocking tunnels on the route. It is also required to evaluate different dispatch policies from the trucks parking lot, ZEAL.

The specific objectives are to evaluate the incidence on the performance indicator of variables such as dispatch frequencies, rate appearance of trucks demand by ship arrivals and route congestion.

This paper provides a modeling approach for dealing with the challenging question of trade-off between capital utilization and production efficiency in semiconductor manufacturing where the ultimate goal is of maximum output at maximum velocity and minimum cost. Full fab simulation is used iteratively between models of “horizontal” and “vertical” simulations in order to rapidly generate results for different possible states of the fab with varying capital costs, matched cycle time (CT), and fixed throughput rate, so as to determine the most efficient operating condition for the fab with respect to cost, CT, and output.

To successfully manufacture logic and power semiconductors in existing high mix semiconductor factories, fast ramp up phases and frequent product changes are necessary. Especially for power semiconductor production, new manufacturing and automation concepts are required, e.g., regarding the use of other substrates than silicon wafers. To allow a judgment on how an existing automated material handling system (AMHS) can cope with the new challenges or which alterations are required, a material flow simulation is essential. However, the planning and creation of such simulation experiments is difficult because of the systems complexity, the large amount of boundary conditions and the effort of manually modifying and testing many different variants, partially using currently unforeseen automation concepts. In order to assist in this process, the authors suggest a method for rapidly creating valid simulation experiments using an ontology that allows for the reuse of previous experiments and system experts knowledge.

With the speedup of product innovation, product life cycles are becoming shorter and shorter. To fully support customers, a foundry fab has to speed up its wafer-output schedule to shorten the time to market in the growth stage. High priority setting and small lot strategy are common techniques for accelerating manufacturing cycle time. However, small lot strategy would accompany 1.2% of extra equipment investment. The improvement should be sufficient so that the extra investment is worthy. Therefore, the novel strategy, dedicated line for small lot size manufacturing, is proposed in this paper, which allocates exclusive resources for expediting lots to greatly improve the manufacturing cycle time. By distinguishing dedicated lines from regular lines, the proposed strategy improves 21.7% of cycle time for expediting lots. Plenty of simulation results can evidence that the proposed strategy significantly outperforms mixed run mode.

Using 3D laser scanning, the spatial data of an entire production system can be captured and digitalized in a matter of hours. Such spatial data could provide a current state representation of the real system available at the hand of the simulation engineer. The purpose of this paper is to evaluate the use of 3D laser scanning in Discrete Event Simulation (DES) projects in the area of production systems. The evaluation relies on three simulation studies performed with the support of 3D laser scanning. 3D scan data, if available, can support most steps in a DES study. Particularly, the 3D scan data acts as a reference model when formulating the conceptual model and collecting input data. During model building the scan data provides physical measurements for accurate positioning of simulation objects. Furthermore the scan data can be used for photorealistic visualization of the simulated environment without requiring any CAD modeling.

This paper introduces a probabilistic algorithm for solving the well-known Facility Location Problem (FLP), an optimization problem frequently encountered in practical applications in fields such as Logistics or Telecommunications. Our algorithm is based on the combination of biased random sampling -using a skewed probability distribution– with a metaheuristic framework. The use of random variates from a skewed distribution allows to guide the local search process inside the metaheuristic framework which, being a stochastic procedure, is likely to produce slightly different results each time it is run. Our approach is validated against some classical benchmarks from the FLP literature and it is also used to analyze the deployment of service replicas in a realistic Internet-distributed system.

The use of process modeling combined with the use of simulation-based analysis provides a valuable way to analyze business processes (BPs) and to evaluate design alternatives before committing resources and effort. The simulation-based analysis of BPs usually addresses performance in terms of efficiency, i.e., focusing on time-related properties (e.g., throughput or execution time). Differently, this paper proposes an automated method for the analysis of BPs in terms of both efficiency-related performance and reliability. In addition, the method allows business analysts to carry out a joint performance and reliability analysis by introducing a so-called performability attribute. The proposed method is illustrated by use of a running example dealing with a conventional e-commerce scenario.

This paper proposes a capacity reservation mechanism for a single-supplier and multi-manufacturer supply chain. The manufacturers first determine the production capacity they should reserve from the supplier, and then realize their reservations and place corresponding supplementary orders within the realization time window. The supplier builds its regular production capacity according to the reservations that have been received, and emergency production capacity for orders that exceed its regular capacity. Towards this end, we develop an analytical model to quantify the manufacturer’s optimal capacity reservation quantity and realization time, as well as the supplier’s optimal regular capacity. Given regular production capacity competition, a Cellular Automata (CA) simulation model is developed to resolve the analytical intractability of reservation realization time by modeling the manufacturers in an N-person game and identifying the convergence condition. Experiment results indicate that the proposed capacity reservation mechanism outperforms the traditional wholesale price contract in a decentralized supply chain.

The dynamic network loading problem is crucial for performing dynamic traffic assignment. It must reproduce the network flow propagation, while taking into account the time and a variable traffic demand on each path of the network. In this paper, we consider a simulation-based approach for dynamic network loading as the best-suited option. We present a multiclass multilane dynamic network loading model based on a mesoscopic scheme that uses a continuous-time link-based approach with a complete demand discretization. In order to demonstrate the correctness of the model, we computationally validate the proposed simulation model using a variety of laboratory tests. The obtained results look promising, showing the model's ability to reproduce multilane multiclass traffic behaviors for medium-size urban networks.

The relative novel discrete rate-based simulation paradigm combines the advantages of the discrete event-based and the system dynamics simulation paradigms. Although its applicability is generally acknowledged in the context of supply chain management, no research works exist, that allow for a direct modeling and simulation of supply chain planning decisions within this paradigm. This paper therefore presents necessary adaptations of the discrete rate-based simulation paradigm for tactical supply chain planning decisions. Our main research contribution lies in extending the discrete rate-based simulation with modeling and material flow controlling mechanisms for enabling a simple implementation and simulation of tactical supply chain planning tasks. To evaluate different planning decisions in this context a multitude of simulation runs are necessary, creating a need for fast simulation approaches. Thus, we show formally, that discrete rate-based simulation models can generally be computed faster than commonly used discrete event-based simulation models.

The following paper describes an overall simulation method for the design of eMobility concepts for rail, road and coastal waters. The presented workflow enables the user to execute the realization process under the usage of real world elevation data and driving profiles. This is essential for the choice of a fitting battery and motorization. Additionally the charging infrastructure has to be chosen without changing anything on the existing driving schedule for electric buses. Taking a look at innovative and sustainable drive concepts completes the overview and indicates the direction for the implementation of eMobility in the near future without the need for big investments. With the shown software approach it is possible to answer the consumers cost questions and reduce their range anxiety for electric vehicles.

Due to rapid urbanization, large cities in developing countries have problems with heavy traffic congestion. International aid is being provided to construct modern traffic signal infrastructure. But often such an infrastructure does not work well due to the high operating and maintenance costs and the limited knowledge of the local engineers. In this paper, we propose a frugal signal control framework that uses image analysis to estimate traffic flows. It requires only low-cost Web cameras to support a signal control strategy based on the current traffic volume. We can estimate the traffic volumes of the roads near the traffic signals from a few observed points and then adjust the signal control. Through numerical experiments, we confirmed that the proposed framework can reduce an average travel time 20.6% compared to a fixed-time signal control even though the Web cameras are located at 500 m away from intersections.

Right-of-way prioritization at unsignalized intersections has been largely unexplored. Drivers do not always use consistent methods to determine who has the right-of-way to enter the intersection at unsignalized intersections. Problems with right-of-way assumptions include that not all drivers engage in one set algorithm to assess intersections priority, and issues of yielding can occur when drivers arrive at an intersection simultaneously or near-simultaneously. A discrete event simulation model was built to emulate a 4-way stop-signed intersection; and different prioritization rules were instated to determine which lane has right-of way. First-in-first-out and yield-to-right prioritization methods were found to differ in terms of time spent waiting and traveling through the intersection, as well as intersection throughput for different intervals of high traffic volume. The first-in-first-out prioritization algorithm provided superior service to drivers arriving at an intersection, compared to the traditional yield-to-right approach, in both low- and high-traffic volume conditions.

In this article, the relationships between virtual metrology and actual measurements are investigated with respect to a sampling decision system (SDS); specifically, a multilevel Virtual Metrology strategy is relied on to provide predictive information. Such virtual measurements serve as input for the sampling decision system, which in turn suggests the optimal measurement strategy. Two approaches relying on decision-theoretical concepts are discussed: the expected value of measurement information (EVofMI) and a two stage sampling decision model. The basic assumption of the SDS-VM system is that it is not necessary to perform a real measurement until it is strictly needed. The two methodologies are then validated relying on simulation studies and actual chemical vapor deposition (CVD) process and measurement data. The ability of the proposed system to sample dynamically the wafer measurements in dependence of the calculated risk is then evaluated and discussed.

Reliable semiconductor devices are of paramount importance as they are used in safety relevant applications. To guarantee the functionality of the devices, various electrical measurements are analyzed and devices outside pre-defined specification limits are scrapped. Despite numerous verification tests, risk devices (Mavericks) remain undetected. To counteract this, remedial actions are given by statistical screening methods, such as Part Average Testing and Good Die in Bad Neighborhood. For new semiconductor technologies it is expected that, due to the continuous miniaturization of devices, the performance of the currently applied screening methods to detect Mavericks will lack accuracy. To meet this challenge, new screening approaches are required. Therefore, we propose to use a data transformation which analyzes information sources instead of raw data. First results confirm that Independent Component Analysis extracts meaningful measurement information in a compact representation to enhance the detection of Mavericks.

In automotive industry end-of-life tests are necessary to verify that semiconductor products operate reliably. Due to limited test resources it is not possible to test all devices and thus, accelerated stress tests in combination with statistical models are commonly applied to achieve reliable forecasts. Challenging thereby is the highly complex data that shows mixture distributions and censoring. For the main purpose, the extrapolation to other test conditions or designs, neither frequently used acceleration models like Arrhenius, nor complex models like Bayesian Mixtures-of-Experts or Bayesian networks give accurate lifetime predictions, although, the latter two are precise in case of interpolation. To compensate the limitations of ordinary regression models, we propose the application of a Gaussian process prior. The proposed model shows a high degree of flexibility by exploiting sums or products of appropriate covariance functions, e.g., linear or exponential, and serves as a reliable alternative to currently applied methods.