WSC 2007 Final Abstracts

An Object-oriented Framework for Simulating Full Truckload Transportation Networks
Manuel Rossetti and Shikha Nangia (University of Arkansas)

Abstract:
In this paper, we discuss the design and use of an object-oriented framework for simulating full truckload (FTL) networks. We present a context for how the framework can be used through its application to an example trucking network. In addition, we describe the design by examining the major conceptual artifacts within the object-oriented model. The framework is built on a Java Simulation Library (JSL) and permits easy modeling and execution of simulation models. The example and discussion indicate the capabilities and flexibility of modeling with the framework. In addition, we summarize our future research efforts to model other transportation networks

Assessing Tram Schedules Using a Library of Simulation Components
Elisangela Mieko Kanacilo and Alexander Verbraeck (Delft University of Technology)

Abstract:
Assessing tram schedules is important to assure an efficient use of infrastructure and for the provision of a good quality service. Most existing infrastructure modeling tools provide support to assess an individual aspect of rail systems in isolation, and do not provide enough flexibility to assess many aspects that influence system performance at once. We propose a library of simulation components that enable rail designers to assess different system configurations. In this paper we show how we implemented some basic safety measures used in rail systems such as: reaction to control objects (e.g. traffic lights), priority rules, and block safety systems.

Supply Chain Simulation Modeling Made Easy: An Innovative Approach
Dayana Cope, Mohamed Sam Fayez, Mansooreh Mollaghasemi, and Assem Kaylani (Productivity Apex, Inc.)

Abstract:
Simulation modeling and analysis requires skills and scientific background to be implemented. This is vital for this powerful methodology to deliver value to the company adopting it. There are several practices to implement and rely on simulation modeling for strategic and operational decision making, including hiring simulation engineers, building internal simulation team, or contract consultants. These practices are different in terms of budget, time to implement, and returns. In this paper, an innovative approach is described that provide a simulation solution that is affordable at the same time can be quickly implemented. it consists of generic interface that captures the information and structure of the supply chain then automatically generates simulation models. The user, which not necessarily a simulation expert, can quickly jump to the analysis and evaluation of scenarios. The paper presents a case study where the approach was implemented to model, simulate, and analyze NASA’s Space Exploration Supply-Chain.

Simulating Air Traffic Blockage Due to Convective Weather Conditions
Liling Ren (Georgia Institute of Technology), Dawei Chang and Senay Solak (Southern Polytechnic State University) and John-Paul B. Clarke, Earl Barnes, and Ellis Johnson (Georgia Institute of Technology)

Abstract:
A Monte Carlo methodology is proposed for simulating air traffic blockage patterns under the impact of convective weather. The simulation utilizes probabilistic convective weather forecasts such as those produced by the 1-6 hour National Convective Weather Forecast. A matrix of random numbers is fed to the simulation process to obtain an instantiation of traffic blockage maps. Gaussian smoothing with varying Full Width at Half Maximum across the grid is employed to model the varying spatial correlation between cells. Special Cellular Automata techniques are employed to model the evolvement, i.e. the trend, growth, and dissipation of convection, between consecutive time intervals. Model parameters are obtained from analyzing historical convective weather data. A software tool is also developed to implement the simulation methodology. The simulation methodology thus provides a means to improve the utilization of short term probabilistic convective weather forecast products, and to improve air traffic efficiency in the large.

Towards a User-centred Road Safety Management Method Based on Road Traffic Simulation
Andreas Gregoriades (University of Cyprus)

Abstract:
One of the most important gaps in road safety management practice is the lack of mature methods for estimating reliability. Road safety performance assessment systems have been developed; however, these provide only historical or retrospective analyses. Effective safety management requires a prospective viewpoint. The main goal of this research is to assist in reducing accident rates in Cyprus by providing ample time to the authorities to react to high risk situations through a safety prediction early warning system. This ultimately will prevent accidents from occurring which subsequently could save lives. Traditional approaches focuses solidly on empirical data concerning road network dynamic properties, despite the fact that the most vulnerable component of the system is the human element. This paper described the integration of agent-based simulation with Bayesian Belief Networks (BBN) for improved quantification of accident probability. The BBN is developed using multidisciplinary influences.

DDDAS-based Multi-fidelity Simulation for Online Preventive Maintenance Scheduling in Semiconductor Supply Chain
Nurcin Koyuncu, Seungho Lee, Karthik K. Vasudevan, Parag Sarfare, and Young-Jun Son (The University of Arizona)

Abstract:
This research intends to augment the validity of simulation models in the most economic way using the DDDAS (Dynamic Data Driven Application Systems) paradigm. Implementation of DDDAS requires automated switching of model fidelity and incorporating selective, dynamic data into the executing simulation model. Comprehensive system architecture and methodologies are proposed, where the components include 1) RT (Real Time) DDDAS simulation, 2) grid computing modules, 3) Web Service com-munication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are developed to facilitate integration of the various components in the DDDAS system. They are 1) data filtering algorithm using control charts, 2) preliminary fidelity selection algorithm using Bayesian belief network, 3) fidelity assignment algorithm using integer programming and 4) simulation model reconstruction algorithm using multiple linear regression. A prototype DDDAS simulation was successfully implemented for preventive maintenance scheduling in a semi-conductor supply chain. The initial results look quite promising.

A Simulation-based Algorithm for Supply Chain Optimization
Takayuki Yoshizumi and Hiroyuki Okano (IBM Research, Tokyo Research Laboratory)

Abstract:
In a supply chain, there are wide variety of problems, such as transportation scheduling problems and warehouse location problems. These problems are independently defined as optimization problems, and algorithms have been proposed for each problem. It is difficult, however, to design an algorithm for optimizing a supply chain simultaneously because the problem is much more complex than the individual problems. We present a simulation-based optimization algorithm that optimizes a supply chain, exploiting both simulation and optimization techniques. This system leverages two existing algorithms, and will optimize a supply chain by executing simulations while changing the boundary conditions between the two algorithms. Experimental results show that a better solution to a supply chain can be found through a series of optimization simulations. A logistics consultant was satisfied with the solution. This system will be used in actual logistics consulting services.

A Toolbox for Simulation-based Optimization of Supply Chains
Christian Almeder and Margaretha Preusser (University of Vienna)

Abstract:
In this paper we present a general framework for simulating and optimizing the operational decisions in a supply chain network. We developed a supply chain network library for the simulation software AnyLogic (© XJ Tech-nologies) and a linearized version as an optimization model implemented using XpressMP (© Dash Optimization). Aggregated results for the simulation experiments are fed into the optimization model. The solution of the optimization model is used to improve operational decision in the supply chain. In order to gain good results this process is repeated until a stable solution is reached. This approach enriches the simulation framework by a powerful tool to improve the supply chain by simultaneously optimizing a large number of possible decisions.

IBM Supply-chain Network Optimization Workbench: An Integrated Optimization and Simulation Tool for Supply Chain Design
Hongwei Ding, Wei Wang, Jin Dong, Minmin Qiu, and Changrui Ren (IBM CRL)

Abstract:
The IBM Supply-chain Network Optimization Workbench (SNOW) is a software tool that can help a company make strategic business decisions about the design and operation of its supply chain network. The tool supports supply chain analysis with integrated network optimization and simulation capability. Mathematical programming models are used to first help identify some cost-effective scenarios from a large number of candidates. Optimization results are then converted to simulation models automatically for more detailed analysis with taking into account operational policies and uncertainties. The tool was applied to analyze both IBM’s internal supply chains and external clients?supply chains. The combination of optimization and simulation demonstrates great value in real business cases.

Using Empirical Demand Data and Common Random Numbers in an Agent-based Simulation of a Distribution Network
William J. Sawaya (Cornell University)

Abstract:
Agent-based simulation provides a methodology to investigate complex systems behavior, such as supply chains, while incorporating many empirical elements relative to both systems structure and agent behavior. While there is a significant amount of simulation and analytical research investigating the impact of information sharing in supply chains, few studies have used empirical demand for the model. This research utilizes empirical distributions in order to determine the demand process faced by distribution centers in a distribution network. Therefore, the distribution centers face independent and heterogeneous demand that is not normal, and exhibits a much larger coefficient of variation than is generally utilized in similar research. With so much complexity and variability, contrasting different inter-organizational information sharing configurations provides an ideal setting for utilizing common random numbers for variance reduction. Comparisons made using this methodology show clear differences between the different information sharing schemes.

A Comparison of Scheduling Approaches for a Make-to-order Electronics Manufacturer
Susan K. Heath (Naval Postgraduate School) and Douglas J. Morrice (University of Texas at Austin)

Abstract:
In this paper, we compare two scheduling procedures designed to minimize setup costs for a make-to-order electronics manufacturing. While setup costs are important, quick response is highly valued by the manufacturer’s customers and customer service is negatively impacted when jobs spend too much time in the system. To address this issue, we simulate the factory running with the schedules produced by these two procedures and compare the output based on the age of jobs remaining unprocessed at the end of one production shift. The simulation results show that the scheduling procedure that results in the lowest setup cost does not necessarily yield the best job age distribution.

Simulation of Scheduled Ordering Policies in Distribution Supply Chains
Lucy G. Chen (NUS Business School) and Srinagesh Gavirneni (Cornell University)

Abstract:
In this paper we study a decentralized distribution supply chain with one supplier and many newsvendor-type retailers that face exogenous end-customer demands. Using total supply chain cost as our primary measure of performance, we compare two scheduled ordering policies - Balanced ordering and Synchronized ordering - with the traditional newsvendor-type ordering behavior. Via the use of simulation, we evaluate the effectiveness of the two scheduled ordering policies, and identify how the performance of the scheduled ordering policies changes with different supply chain parameters, such as the number of retailers, the supplier's expediting cost, the supplier's capacity limit, etc.

Stability Analysis of the Supply Chain by Using Neural Networks and Genetic Algorithms
Alfonso Sarmiento and Luis Rabelo (University of Central Florida), Reinaldo Moraga (Northern Illinois University) and Ramamoorthy Lakkoju (University of Central Florida)

Abstract:
Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology.

A Supply Chain Paradigm to Model Business Processes at the Y-12 National Security Complex
Reid Leonard Kress, Jack Dixon, Tom Insalaco, and Richard Rinehart (BWXT Y-12)

Abstract:
The NNSA’s Y 12 National Security Complex is a manufacturing facility operated by BWXT Y 12. Y-12’s missions include ensuring the US’ nuclear weapons deterrent, storing nuclear materials, and fueling US naval reactors. In order to understand the impacts of these diverse missions on its numerous functional divisions, Y-12 has relied on simulation modeling. Traditional discrete-event simulation modeling has proven to be an indispensable tool for Y-12; however, this paper will discuss Y-12’s use of a supply chain paradigm to model its entire business processes. The supply chain model executes very quickly and is versatile enough to model all of the nuances of Y-12’s complex business. It can model equipment, labor, facility, or other constraints and provides a rough-cut estimate of schedule compliance over many years (even decades). This paper describes how the model is implemented and presents simple results from a representative process.

Appraisal of Airport Alternatives in Greenland by the Use of Risk Analysis and Monte Carlo Simulation
Kim Bang Salling and Steen Leleur (Technical University of Denmark)

Abstract:
This paper presents an appraisal study of three different airport proposals in Greenland by the use of an adapted version of the Danish CBA-DK model. The assessment model is based on both a deterministic calculation by the use of conventional cost-benefit analysis and a stochastic calculation, where risk analysis is carried out using Monte Carlo simulation. The feasibility risk adopted in the model is based on assigning probability distributions to the uncertain model parameters. Two probability distributions are presented, the Erlang and normal distribution respectively assigned to the construction cost and the travel time savings. The obtained model results aim to provide an input to informed decision-making based on an account of the level of desired risk as concerns feasibility risks. This level is presented as the probability of obtaining at least a benefit-cost ratio of a specified value. Finally, some conclusions and a perspective are presented.

A Simulation Study on the Uses of Shuttle Carriers in the Container Yard
Loo Hay Lee, Ek Peng Chew, Kok Choon Tan, Huei Chuen Huang, Wenquan Lin, and Yongbin Han (National University of Singapore) and Tian Heong Chan (PSA International Pte Ltd)

Abstract:
In this paper, we investigate how two main factors affect the efficiency of the port operation. The two main factors are type of transport vehicles and layout of the storage yard. Two different types of transport vehicles (i.e., prime mover and shuttle carrier) and two different types of layouts (i.e., with or without chassis lane beside the container blocks) are modeled in this study. A total of four simulation models are created to conduct this study. To evaluate the performance, the gross crane rate is used as the main performance measure, which is defined as the number of containers moved per quay crane per working hour. In this paper, it has been shown that the incorporation of the chassis lane improves the gross crane rate for both prime movers and shuttle carriers. The improvement is more substantial when the port utilizes shuttle carriers.

A Simulation Model with a Low Level of Detail for Container Terminals and Its Applications
Byung-Hyun Ha (Pusan National Unversity) and Eun-Jung Park and Chan-Hee Lee (Pusan National University)

Abstract:
As trade among countries grows, the performance of container terminals is becoming more important than ever. In this paper, we present a 3D real-time-visualization container-terminal simulation model based on Plant Simulation, a commercial simulation modeling and execution tool. Our model reproduces every detailed behavior of container-terminal equipment, including not only movements of yard tractors and cranes but also those of trolleys, spreaders, and other machinery. Such low-level representation enables our simulation model to be easily visualized in 3D form and to offer real-time interactive capability. We analyzed the performance of container terminals by varying the settings such as the speeds of trolleys and spreaders, in detail. The simulation model in this study is expected to be useful for assessment of the effects of prospective new equipment on the performance of container terminals and, thereby, for decision-making on the implementation of such equipment.

A Simulation Model to Improve Warehouse Operations
Jean Philippe Gagliardi, Jacques Renaud, and Angel Ruiz (Universite Laval)

Abstract:
Warehouse or distribution centre managers have to decide how to collect the products to fulfill customers requests but also where to locate the products (SKUs) and how much space to allocate to each of them. Moreover, they have to deploy replenishment strategies to guarantee the reliability of their own stocks. These are challenging decisions because of their level of complexity and their high impact on the centre performance in terms of both its throughput and the operation costs. The goal of this work is to evaluate whether specific strategies to share the storage space could lead to reduce the operation costs while keeping the service level as high as possible. This paper develops a discrete event simulation model of the logistics operations at a real warehouse. Preliminary results show that potential economies may be achieved by reducing the number of stock-outs at the picking area where customer orders are collected.