WSC 2008

WSC 2008 Final Abstracts

PHD Colloquium Track

Sunday 1:00:00 PM 2:00:00 PM
Ph.D. Colloquium Luncheon & Plenary

Chair: Soumyadip Ghosh (IBM)

Staying Sane on the Tenure Track
Shane G. Henderson (Cornell University)

A tenure-track appointment is a wonderful thing, but it really should come with an instruction manual. This article is a loosely-coupled collection of thoughts and advice on surviving a tenure-track appointment. The focus is on concrete tips and advice for an engineering-school appointment, which was my path, but perhaps some of the ideas are also valid in other environments, such as business schools and mathematics or computer science departments.

Sunday 2:30:00 PM 4:00:00 PM
Ph.D. Colloquium Student Presentations

Chair: Soumyadip Ghosh (IBM)

Robust Simulation-Optimization using Kriging Metamodels
Gabriella Dellino (University of Bari - Dept. of Mathematics)

Simulation-optimization aims to identify the setting of the input parameters of a simulated system leading to optimal system performance. In practice, however, the computed optimum may turn out to be suboptimal or infeasible because the environment does not meet the assumptions; e.g., the demand's expected value turns out to be different. A possible solution is offered by robust optimization (RO), which aims at deriving solutions that are relatively insensitive to perturbations caused by the environmental or noise factors. The proposed method combines the Taguchian view of the uncertain world with Kriging metamodels and Mathematical Programming. It is applicable to both deterministic and stochastic simulation models; in particular, it has been applied in the context of Supply Chain Management, starting from some building blocks such as the Economic Order Quantity (EOQ) and the (s,S) inventory models.

Integrated Human Decision Making Model under Belief-Desire-Intention Framework for Crowd Simulation
Seungho Lee (The University of Arizona)

An integrated Belief-Desire-Intention (BDI) modeling framework is proposed for human decision making and planning, whose sub-modules are based on Bayesian belief network (BBN), Decision-Field-Theory (DFT), and probabilistic depth first search (PDFS) technique. To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from the human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for human’s evacuation behaviors under a terrorist bomb attack situation. The simulated environment and agents (human model) conforming to the proposed BDI framework are implemented in AnyLogic agent-based simulation software, where each agent calls external Netica BBN software to perform its perceptual processing function and Soar software to perform its real-time planning and decision-execution functions. The constructed simulation has been used to test impact of several factors (e.g. demographics of people, number of policemen) on evacuation performance (e.g. average evacuation time, percentage of casualties).

Patterns of Exploration and Exploitation of Organizational Knowledge: An Investigation Using Agent-Based Modeling
Srikanth Mudigonda (University of Missouri-St. Louis) and Rajiv Sabherwal (University of Missouri-St.Louis)

This proposed poster examines the initial results of an agent-based simulation that builds on the March (1991) model of knowledge exploration and exploitation in organizations. In our agent-based model, agents access knowledge from other agents and electronic knowledge repositories. The simulation is conducted under three broad conditions to examine how they differ in the emergence of consensus and diversity of knowledge: (a) a knowledge-based model, wherein dyadic exchange of knowledge is modeled as a function of the perceived expertise of source and recipient agents; (b) a cohesion-based model (Burt 1987), wherein knowledge spreads between any two agents only if they have direct contact with one another; and (c) a structural equivalence-based model, wherein knowledge spreads between agents that are structurally equivalent (Burt 1987). Finally, we examine how the emergence of consensus and diversity of knowledge depends on a) environmental turbulence, b) employee turnover, and c) use of communication technologies.

Human Behavior Representation in Physical Security Systems Simulation
Volkan Ustun (Auburn University)

The goal of this research is to develop an agent directed simulation based problem solving environment, and associated decision support tools to assist with the general physical security systems design problems. Realistic and credible simulations of physical security systems require incorporation of human behavior models. The primary contributions include: (1) A conceptual facility configuration meta-model named Hierarchical Graph Representation for Scenes (HIGHRES) for flexible instantiation of environmental settings in which agents are situated, (2) A Behavior-Intuition Framework for Realistic Agents (ABIRA) to model the reactive as well as deliberate decision making processes of realistic agents, and (3) A comprehensive vision-based perception and recognition model to capture the interactions between the agents and between the agents and the environment. A hypothetical retail store security system design problem is used to demonstrate the capabilities of the proposed approach and to validate the realistic human behavior generation framework.

A Particle Filtering Framework for Randomized Optimization Algorithms
Enlu Zhou, Michael C. Fu, and Steven I. Marcus (University of Maryland, College Park)

We propose a framework for optimization problems based on particle filtering (also called Sequential Monte Carlo) method. This framework unifies and provides new insight into randomized optimization algorithms. The framework also sheds light on developing new optimization algorithms, through the freedom in the framework, and the various improving techniques for particle filtering.

Simulation of Stochastic Hybrid Systems with Switching and Reflecting Boundaries
Derek Riley and Xenofon Koutsoukos (Vanderbilt University) and Kasandra Riley (Yale University)

Modeling and simulation of biochemical systems are important tasks because they can provide insights into complicated systems where traditional experimentation is expensive or impossible. Stochastic hybrid systems are an ideal modeling paradigm for biochemical systems because they combine continuous and discrete dynamics in a stochastic framework. Simulation of these systems is difficult because of the inherent error which is introduced near the boundaries. In this work we develop a method for stochastic hybrid system simulation that explicitly considers switching and reflective boundaries. We also present a case study of the water/electrolyte balance system in humans and provide simulation results to demonstrate the usefulness of the improved simulation techniques.

Cycle Time Prediction for Semiconductor Manufacturing via Simulation on Demand
Bruce Ankenman, Barry Nelson, and Mustafa Hayri Tongarlak (Northwestern University), John Fowler, Gerald Mackulak, and Detlef Pabst (Arizona State University) and Feng Yang (West Virginia University)

Traditionally, competition between semiconductor manufacturers has primarily focused on product design and cost. Recently, speed of delivery has also become an important differentiator among these firms which has led to manufacturing cycle time becoming a critical performance measure. This paper presents a methodology that performs a limited set of simulation runs for a complex wafer fabrication system, and then uses the results to develop metamodels that predict mean steady-state cycle time as a function of product mix and throughput. These predictions can be made on demand, i.e., without performing any additional simulation runs, for product mixes and throughput levels not previously simulated. The goal is to support medium and long range planning by providing results with the fidelity of a detailed simulation model, but with the speed of a queueing approximation or simple capacity model.

Military Operational Analysis Tool “Sandis”
Esa Lappi (Finnish Defense Forces Tecnical Research Centre)

Sandis is a novel military OA tool used by Finnish Defense Forces (FDF) for comparative combat analysis from platoon to brigade level. The software is based on Markovian combat modeling and fault logic analysis. The input of the tool is weapon and communication characteristics, units and their weapons, fault logic for units and operation success, map and user actions for units in company or platoon level. The output is the operation success probability, probability for each unit to get beaten, unit strength distributions, average combat losses and the killer-victim scoreboard, ammunition consumption, radio network availability and medical evacuation logistics and treatment capacity analysis. Sandis has been used since year 2006 for peace time cost – effect analysis and it is tested for task planning of wartime headquarters. The software has been coded in FDF Technical Research Center and the model is part of doctoral studies in National Defense University.

Using a Simulated Epidemiology Model to Visualize Public Health Policies for the Next Pandemic Influenza
Ozgur Araz (Arizona State University), Timothy Lant (Decision Theater at Arizona State University), Megan Jehn (W.P. Carey School of Business Arizona State University) and John Fowler (Industrial Engneering Department Arizona State University)

In this research, the simulation of a mathematical epidemiology model with policy incorporation is presented. The model includes the population behaviors and the effects of pandemic influenza on a public university community. The system is simulated for multiple non-pharmaceutical interventions with several policies that can be employed by the local decision makers to give them an opportunity to visualize their policies through the simulations. System components are constructed from the pandemic influenza preparedness plan of one of the largest universities in the country. The policies and the decisions are tested by simulation runs and evaluations of the mitigation strategies are presented.

Factors and Forces Guiding Telecommunication Development Towards the Accruement of Social and Economic Benefits
Zenzo Polite Ncube, Johannes Michael Hattingh, and Albert SJ Helberg (North West University)

There is a general consensus in the world that telecommunication technology can be instrumental to improve previously unheard of benefits to societies, both small and large business enterprises and eventually to economic growth. In the developing world, there is a severe lack of fixed line telecommunication infrastructure and many researchers believe that the advent of cellular communications and wireless technology can help such developing countries to “leap frog” towards a “better life” using these methods. The aim of this study is to consider these factors by doing international comparisons based on data obtained from the ITU, World Bank and other sources. The methodology that will be applied is that of data modeling by multiple regression techniques and the use of interpretive techniques like Linear Response Surface Analysis.

Sunday 4:00:00 PM 7:00:00 PM
Ph.D. Colloquium Posters

Chair: Soumyadip Ghosh (IBM)

Techniques for Enhancing System Understanding Through Simulation
Kara A. Olson and C. Michael Overstreet (Old Dominion University) and E. Joseph Derrick (Radford University)

Simulation models are built for many purposes including design, training and enhanced understanding of systems of interest. We are interested in helping both model builders and model users better understand their models as greater understanding of the models often leads to greater understanding of the systems being simulated. We report on ongoing efforts to use traditional software engineering code analysis techniques applied to model specifications in order to enhance system understanding. Some results from analysis efforts using CS-XML (Olson, Overstreet and Derrick 2007), an XML-based model specification language, and CodeSurfer (Anderson et al. 2003), a software static analysis tool, are presented.

Toward a Model for Emergency Department Wait Times in a Mexican Public Hospital
Rodolfo Medina and Antonio Vázquez (Universidad Politécnica de Aguascalientes) and Héctor Juárez and Ricardo González (CU Lagos/Universidad de Guadalajara)

Public health care services are facing a growing demand, in a context where public funds to these services are being stretched. Public Hospitals should find a way to optimize use of resources and improve the quality of services being offered. Even though this conference has documented successful experiences with simulation through the years, it has also opened discussion to reach a general, robust model to face emergency department challenges successfully. This paper presents a brief state of the art around the world, a brief review of simulation work done in Mexican Public Health Care System, and finally a proposal to improve these services using simulation.

Integration of Computer-Based Training in Truck Driving Training Program
Alpesh P. Makwana and Jia Luo (Institute for Simulation and Training, University of Central Florida)

Pre-Trip Inspection of the truck and trailer is one of the components of the current CDL (Commercial Driver’s License) test. Operating a large truck and doing pre-trip inspection is being taught at a truck driving training program. Majority of the truck driver training programs involve combination of classroom lectures and supervised driving. Some training organizations are introducing high tech approaches such as simulation and computer-based instruction into their curriculum to improve students’ performance. However, use of high tech approaches may not be cost-effective, especially considering the price of a simulator. A simulator may prove beneficial in practicing hazardous conditions and emergency situations but may not be useful in doing pre-trip inspection. This presentation will illustrate CDL trucks involved in crash; current training procedures in truck driving program; and demonstrate the need of a cost-effective computer-based application that has an assessment and feedback tool in pre-trip inspection.

Simulation as a Planning and Decision-Making Tool in the Context of Hospitality Operations Management
Alinda Kokkinou and Breffni Monica Noone (The Pennsylvania State University)

Hospitality organizations are increasingly using self service technology to improve customer service and reduce costs. Nevertheless little information is available on how the introduction of self service technology impacts these performance measures. Since hospitality services are complex systems, their behavior is difficult to model using traditional techniques such as queuing theory. We develop a computer-based simulation model designed to enable hotel operators to evaluate the impact of self service usage for front office functions including check-in, check-out and concierge services, on front office costs and customer service levels. We look at how operator characteristics (number of staff and self service kiosks) and customer characteristics (comfort with technology, time pressure) affect the system. The tool is designed to be used as a black box application by individuals unfamiliar with simulation.

Real-time estimation and prediction of performance measures along signalized arterials with the aid of run-time infrastructure and traffic simulation technologies
Dwayne Anthony Henclewood (Georgia Institute of Technology)

Congestion is one of the major issues facing today’s transportation sector. Recent efforts have been geared toward providing more traffic information to travelers and transportation facility managers to promote better decisions regarding mobility. Currently, real-time traffic information is limited to freeways and a small subset of major arterials. This effort is geared towards developing a tool that uses point sensor data to address the lack of real-time arterial performance measures. Additionally, snapshots of the current simulated world will be used to create other simulations to run faster than real-time to estimate future conditions and propose measures to mitigate undesirable traffic conditions. This tool uses a run-time infrastructure platform to handle field data that will in turn be used by a traffic simulation package, VISSIM, to model operations. Preliminary analysis indicates that the considered approach is feasible, where a model of the “real-world” proves capable of accurately reflecting key performance measures.

Applying Causal Inference to Understand Emergent Behavior
Ross Gore (University of Virginia)

Emergent behaviors in simulations require explanation, so that valid behaviors can be separated from design or coding errors. Validation of emergent behavior requires accumulation of insight into the behavior and the conditions under which it arises. Previously, I have introduced an approach, Explanation Exploration (EE), to gather insight into emergent behaviors using semi-automatic model adaptation. I improve the previous work by iteratively applying causal inference procedures to samples gathered from the semi-automatic model adaptation. Iterative application of causal inference procedures reveals the interactions of identified abstractions within the model that cause the emergent behavior. Uncovering these interactions gives the subject matter expert new insight into the emergent behavior and facilitates the validation process.

Stationarity Tests and MSER-5: Exploring the Intuition Behind Mean-Squared-Error-Reduction in Detecting and Correcting Initialization Bias
William Franklin and K. Preston White (University of Virginia)

We explore the reasoning behind MSER-5, an efficient and effective truncation heuristic for reducing initializa-tion bias in steady-state simulation. We also compare MSER-5 with the KPSS stationarity test as one means of investigating the possibility that MSER’s effectiveness is the result of its utility as a stationarity measure. Con-versely, this comparison also lets us explore whether or not a stationarity test from the time-series literature can be used as an effective initialization bias-control heuristic. Finally, we investigate the use of an alternative form of MSER-5 that uses a variance estimator that adjusts for se-rial correlation.

Multi-agent Transport Simulation of South African Commuters
Pieter J. Fourie (University of Pretoria)

Our research group investigated the capability of the transport microsimulation package, MATSim, to capture the unique dynamics that emerge in the South African metropolitan context. Our initial implementation models the passenger vehicle traffic of the Gauteng province, South Africa's densely populated economic hub. Initial results are promising, with simulated traffic counts on important road network links closely following the trends observed in reality during the course of a day. The poster illustrates the development of the initial implementation, and focuses on the procedures followed to interpret and transform source data into a format suitable for MATSim. In particular, the generation of a spatially distributed synthetic population from South African census data, and the assignment of day activities for that population proved to be key to success. Noteworthy results are presented and analysed, and further improvements as well as the longer-term development plans of the implementation are discussed.

Innovative Shipbuilding Processes Incorporating Flexibility
Fang Dong (University of Michigan, IOE), David J. Singer (University of Michigan) and Mark P. Van Oyen (University of Michigan, IOE)

U.S. shipbuilders produce the finest warships in the world, but cost growth is eroding the purchasing power of the Navy. High variability in production workload, ineffective production control, and lack of design for supply chain resilience characterize traditional ship production. To remedy this, we introduce flexibility to ship production via flexible block-building shops. We provide a simulation model as a testbed for production scheduling rules. We also present our development of a stochastic model to support the development of an effective dynamic block production control policy with the objective of minimizing production delay.

Scheduling Multi-skill Call Centers
Wyean Chan (Université de Montréal)

Multi-skill call center scheduling optimization is much more difficult than the single-skill scheduling or single-period staffing version for several reasons such as the presence of skill overlaps, more complex routing policies and stochastic elements, and a much larger number of integer variables. Common practice is to first solve the simpler single-period staffing problems independently, then solve the shift-covering problem based on the staffing results. However, this simplified approach generally does not perform as good as if the scheduling problem was solved globally. We present an algorithm using linear cuts and simulations, followed by some local search methods that typically performs better than the two-step approach.

Speeding Up the Simulation of Multiple Configurations of a Call Center Using Split and Merge
Eric Buist and Pierre L'Ecuyer (Université de Montréal)

We estimate the service level in a multi-skill call center for multiple staffing vectors using a split and merge simulation technique. If a slight change of the staffing only affects a small part of the simulation horizon, a split and merge technique reuses simulation work when estimating performance for several staffings. The method we use assumes that the evolution of the system depends on the staffing only through a finite number of decision points. It simulates parallel replications which can split at decision points, and merge when states are equal. However, this method is efficient only if the model's state can be cloned quickly. We apply the method on a simplified call center model based on a continuous-time Markov chain with uniformization. The state space is multi-dimensional, and the service level depends on the call-by-call waiting times. The resulting simulator is faster than an equivalent program using discrete events.

Control Variate Technique: A Constructive Approach
Tarik Borogovac and Pirooz Vakili (Boston University)

The technique of control variates requires that the user identify a set of variates that are correlated with the estimation variable and whose means are known to the user. We relax the known mean requirement and instead assume the means are to be estimated. We argue that this strategy can be beneficial in parametric studies, analyze the properties of controlled estimators, and propose a class of generic and effective controls in a parametric estimation setting. We discuss the effectiveness of the estimators via analysis and simulation experiments.

Multi-echelon Joint Maintenance and Service Parts Inventory Policies: A Multiobjective Optimization Approach
Oscar E Martinez (University of Central Florida)

Service parts are intended to assist maintenance in keeping equipment in operating conditions, therefore maintenance and service parts inventory policies are highly related. However they are usually addressed separately. We develop joint maintenance and service parts model for a two-echelon service parts supply system where a supplier services several customers. This model is then optimized using both individual and multi-objective approaches. The two approaches are compared to demonstrate the benefits of the multi-objective optimization approach. Later, the model is extended to include lateral transshipments between customers and optimized using a multi-objective approach to demonstrate how the performance of the supply chain is improved.

Distributed Simulation for the Design and Analysis of Adaptive Supply Chains
Shanshan Wang, Shao-Jen Weng, Tong (Teresa) Wu, and John Fowler (Arizona State University) and Blair Binney and Steve J Buckley (IBM)

Distributed simulation is an emerging technique in the field of simulation. Since distributed simulation supports model reusability, a model of a complex system can be easier built from a number of small simulations compared to the development of a traditional (monolithic) Discrete Event Simulation (DES) model. In this research, we implement a distributed simulation with nine individual federates to study a supply chain for a computer manufacturer. These federates model the entities in the different levels of the company’s Server/Storage Fulfillment Supply Chain. Using this distributed simulation, we conduct three experiments to study the distributed decision support framework. The experiment topics cover supplier exceptions, order monitoring, and decentralized/centralized order fulfillment planning. The distributed simulation has proven valuable to gain managerial insights in making consistent decisions on a global level to meet customer orders adaptively. It also helps better capture the dynamics in the adaptive supply chain in response to internal and external disruptions.

Distributed Agent-Based Simulation of Construction Projects with HLA
Hosein Taghaddos (University of Alberta)

Simulation techniques can provide a resource-driven schedule and answer many hypothetical scenarios before project execution to improve on conventional project management software applications for large-scale construction projects. However, the current process of simulation and optimization of resource utilization is a time consuming process especially for large-scale projects. This study employs High Level Architecture (HLA) to develop distributed agent based simulation models. These models are composed of several individual modeling components (federates) that can cooperate with each other for the simulation model (interoperability). These federates are developed in a generic way for reuse on future construction projects. A number of agent-based federates are considered for managing various aspects of the project and to enhance the performance of the simulation model. This framework is illustrated using two case studies, module assembly yard and tower crane, that investigate the feasibility of the proposed approach.

Skart: A Skewness- and Autoregression-Adjusted Batch-Means Procedure for Simulation Analysis
Ali Tafazzoli (North Carolina State University)

We discuss Skart, an automated batch-means procedure for constructing a skewness- and autoregression-adjusted confidence interval for the steady-state mean of a simulation output process. Skart is a sequential procedure designed to deliver a confidence interval that satisfies user-specified requirements concerning not only coverage probability but also the absolute or relative precision provided by the half-length. Skart exploits separate adjustments to the half-length of the classical batch-means confidence interval so as to account for the effects on the distribution of the underlying Student’s t-statistic that arise from nonnormality and autocorrelation of the batch means. Skart also delivers a point estimator for the steady-state mean that is approximately free of initialization bias. In an experimental performance evaluation involving a wide range of test processes, Skart compared favorably with other simulation analysis methods—namely, its predecessors ASAP3, WASSP, and SBatch as well as ABATCH, LBATCH, theHeidelberger-Welch procedure, and the Law-Carson procedure.

Analysis of Coverage Functions for Sequential Stopping Rules
Devaushi Singham and Lee Schruben (University of California, Berkeley)

Sequential stopping rules are often used to generate confidence interval estimates in simulation output analysis. Though these methods achieve nominal coverage asymptotically, in practice ad hoc adjustments may be required to obtain adequate coverage. This research attempts to develop a generally applicable framework that would quantify the loss of coverage and propose a means of obtaining improved coverage for stopping rules through derivation of coverage functions. The stopping rules are applied to observations that are assumed to be independent and normally distributed. We derive analytically the coverage function for any given stopping rule and calculate several examples numerically. The results are very close to empirical tests of stopping rules, suggesting that this framework could be used to mitigate the loss in coverage. The distribution of the number of simulations required to meet the stopping rule is derived and provides information on the computational cost of the procedure.

Research Leading To A Methodology For Domain Specific Simulation
Kitti Setavoraphan and Floyd H. Grant (University of Oklahoma)

In the modeling and simulation (M&S) arena, simulation developers have been exploring the concepts that facilitate modeling real world elements using appropriate simulation artifacts. However, there are some critical issues that distort their effectiveness and efficiency. The first issue is the quantity and quality of assumptions and constraints made during the M&S development, concerning the completeness of simulation models to represent reality. The second issue is the levels of model composability and simulation interoperability, affecting the possibility of data exchange and reusability. The third issue is the simulation-based environment that the implementation of the concepts is undertaken, limiting the expressiveness of use. Thus, this research study aims to develop a methodology that addresses these issues to improve the M&S development. Conceptual simulation modeling (CSM), model transformation, and domain specific simulation environment (DSSE) create the foundations for this methodology to bridge the gap between reality and simulation.

Monotonicity and Stratification
Gang Zhao (Boston university)

In utilizing the technique of stratification, the user needs to first partition/stratify the sample space; the next task is to deter-mine how to allocate samples to strata. How to best perform the second task is well understood and analyzed and there are effective and generic guidelines for sample allocation. Performing the first task, on the other hand, is generally left to the user who has limited guidelines at her/his disposal. We review explicit and implicit stratification approaches considered in the literature and discuss their relevance to simulation studies. We then discuss the different ways in which monotonicity plays a role in optimal stratification.

Simulation Based Optimization of (s, S) Policy for Multi-Location Inventory Problem with Capacitated Transshipments
Banu Y. Ekren and Sunderesh S. Heragu (University of Louisville)

In this paper, an (s, S) inventory system in which the items can be stored at any of N stocking locations and shipped to the others (emergency lateral transshipment) is optimized using simulation. The objective function of the problem minimizes the total inventory, backorder, order and transshipment costs. Decision variables are reorder point (s) and order up to quantity (S). In the problem, we consider fixed and variable ordering costs and stochastic replenishment lead times. We also assume that the transportation capacities at the stocking locations are bounded by transshipment policies. Assuming stochastic demand, the system is modeled based on different cases of transshipment capacities and costs. To find out the effect of a transshipment policy on stocking locations and the optimum (s,S) levels, the simulation model of the problem (developed using ARENA 10.0) is optimized using the OptQuest tool.

How Much is a Health Insurer Willing to Pay for Colorectal Cancer Screening Tests?
Reza Yaesoubi and Stephen D. Roberts (North Carolina State University)

Colorectal Cancer (CRC) screening tests have proven to be cost-effective in preventing cancer incidence. Yet, as recent studies have shown, CRC screening tests are noticeably underutilized. Among the factors influencing CRC screening test utilization, the role of health insurers has gained considerable attention in recent studies. In this paper, we propose an analytical model for the market of CRC screening tests. We show how the insurer can benefit from a computer simulation model to cope with the problem of incomplete and asymmetric information inherent in this market. Our estimates reveal that promoting CRC screening tests is not necessarily economically attractive to the insurer, unless the insurer’s valuation of life is greater than a certain limit. We use the proposed model to estimate such a threshold – the insurer’s willingness-to-pay to acquire one additional life year by covering the CRC screening tests.

Mixed Model Assembly Line Balancing Problem with Fuzzy Operation Times and Drifting Operations
Weida Xu and Tianyuan Xiao (National CIMS Engineering Research Center)

Assembly line balancing boils down to assigning a series of task elements to uniform sequential stations under certain restrictions. This paper considers a specific type of assembly line balancing problem, with mixed models, fuzzy operation times and drifting operations. The objective is to minimize the total work overload time. According to chance constrained programming, a fuzzy alpha total work overload time minimization model is built. Moreover, fuzzy simulation and genetic algorithms are integrated in the design of a hybrid intelligent algorithm for solving the model. Finally, extensive computational results are reported to demonstrate the efficiency and effectiveness of the algorithm.

Agent-based acoustic model for acoustic environment simulation in hospitals
Hui Xie and Jian Kang (University of Sheffield)

Noise, defined as unwanted sounds, is annoying and is physiologically and psychologically stressful. Unfortunately, many case studies show that noise levels in hospitals are typically over 15dBA higher than the guidelines. Noise is often on the top list of complaints by patients and staff, whereas little work has been done to characterise and reduce hospital noise. Building high-quality acoustic environment simulations in hospitals has several challenges. Generally acoustic software focuses on single space, rather than multi-spaces. Multiple and dynamic noise sources will present another technical difficulty. Further complications arise in a real hospital environment. Agent-based modeling is a new and useful approach to modeling systems comprised of interacting autonomous agents. In this paper we present our work on an agent-based approach to acoustic environment simulation in hospitals. This should assist in creating a more comfortable acoustic environment and improve the patients’ health.

Queueing Models for Single Machine Manufacturing Systems with Interruptions
Kan Wu (Georgia Tech)

Queueing theory is a well-known method for evaluating the performance of manufacturing systems. When we want to analyze the performance of a single machine, M/M/1 queues or approximations of G/G/1 queues often are considered a proper choice. However, due to the complex nature of interruptions in manufacturing, the appropriate model should be selected carefully. This paper proposes a systematic way to classify different kinds of interruptions found in a single machine system. Queueing models for each category are proposed, and event classifications are compared from both the SEMI E10 and queueing theory points of view.

PiDES - A Formalism for Modeling and Simulation of Complex Adaptive Systems
Jianrui Wang (Penn State University)

A formalism is a powerful tool for precisely defining Discrete Event Systems (DES). Conventional formalisms, such as GSMP, DEVS, and Petri Net, have proved useful for modeling individual systems. However, they become ineffective for some large scale complex adaptive systems due to the requirements of: a) compositing heterogeneous systems into larger ones; b) coordinating distributed systems; and c) evolving existing systems into new ones. This thesis proposes a new DES formalism, called PiDES. It develops formal models for individual DES federates and runtime infrastructure based on π-calculus and the High Level Architecture. In order to demonstrate the feasibility and potential benefits of the proposed formalisms, a compiler of PiDES and a prototype implementation of PiDES-RTI are also developed. The major contribution of this research is to provide a unified approach to modeling and coordinating large complex simulation systems with rigorous semantics, high re-configurability, and seamless scalability.

A Generic Framework for Real-Time Discrete Event Simulation (DES) Modeling
Siamak Tavakoli, Alexander Komashie, and Alireza Mousavi (Brunel University)

This paper suggests a generic simulation platform that can be used for real-time discrete event simulation modeling. The architecture of the proposed system is based on a tested flexible input data architecture developed in Lab-view, a real-time inter-process communication module between the Labview application and discrete event simulation software (Arena). Two example applications in the healthcare and manufacturing sectors are provided to demonstrate the ease of adaptability to such physical systems.