WSC 2002

WSC 2002 Final Abstracts

General Applications and Methodology Track

Monday 1:30:00 PM 3:00:00 PM
General Methodology 1

Chair: Saeid Nahavandi (Deakin University)

A Robust Simulation-Based Multicriteria Optimization Methodology
Raid Al-Aomar (CSI)

This paper describes a methodology for solving Parameter Design (PD) problems in production and business systems of considerable complexity. The solution is aimed at determining optimum settings to system critical parameters so that each system response is at its optimum performance level with least amount of variability. When approaching such problem, analysts are often faced with four major challenges: representing the complex parameter design problem, utilizing an effective search method that is able to explore the problem’s complex and large domain, making optimization decisions based on multiple and, often, conflicting objectives, and handling the stochastic variability of in system response as an integral part of the search method. to tackle such challenges, this paper proposes a solution methodology that integrates four state-of-the-art modules of proven methods: Simulation Modeling (SM), Genetic Algorithm (GA), Entropy Method (EM), and Robustness Module (RM).

A Handbook for Integrating Discrete Event Simulation as an Aid in Conceptual Design of Manufacturing Systems
Mats Jägstam (University of Skövde) and Pär Klingstam (Volvo Car Corporation)

Despite significant cost savings and the stride towards developing and implementing the Virtual Factory, few companies have managed to fully integrate simulation as a daily tool in their engineering processes. The objective of the paper is to explore the pre-requisites for this integration, using Discrete Event Simulation as an aid for high quality decision making in early phases (conceptual design and pre-study). The paper looks at three aspects of the pre-requisites: technological, operational, and organizational and summarizes the main challenges connected to each one of the aspects. The main result presented in the paper is a proposal for a simulation handbook, to be used when integrating simulation into the engineering process. The strength of the handbook is the focus on operational and organizational issues, reflecting different roles with connection to simulation. Future work aims at validating the impact of the handbook.

Optimising Discrete Event Simulation Models Using a Reinforcement Learning Agent
Douglas C. Creighton and Saeid Nahavandi (Deakin University)

A reinforcement learning agent has been developed to determine optimal operating policies in a multi-part serial line. The agent interacts with a discrete event simulation model of a stochastic production facility. This study identifies issues important to the simulation developer who wishes to optimise a complex simulation or develop a robust operating policy. Critical parameters pertinent to 'tuning' an agent quickly and enabling it to rapidly learn the system were investigated.

Monday 3:30:00 PM 5:00:00 PM
General Methodology 2

Chair: Jean-Baptiste Filippi (University of Corsica)

A Comparison of Selective Initialization Bias Elimination Methods
Jennifer R. Linton (Accenture) and Catherine M. Harmonosky (Penn State University)

When simulating a non-terminating system, the issue of initialization bias must be addressed. Many approaches have been developed to remove initialization bias from the output data. This paper provides a comparison of 5 selected methods applied to two slightly different 2-machine flow shop models. The experiment tests for statistical differences between mean and variance of the data used by each method to calculate steady state performance measures. Additionally, for each method, the practicality and ease-of-use for general applicability in larger modeled environments is discussed.

An Efficient Method for Simulating Fractional Stable Motion
Wei Biao Wu (University of Chicago) and George Michailidis and Danlu Zhang (University of Michigan)

An efficient methodology for simulating paths of fractional stable motion is presented. The proposed approach is based on invariance principles for linear processes. An detailed analysis of the error terms involved is given and the performance of the method is assessed through an extensive simulation study.

Enabling Large Scale and High Definition Simulation of Natural Systems with Vector Models and JDEVS
Jean-Baptiste Filippi and Paul Bisgambiglia (University of Corsica)

This paper describes a new methodology to enable large scale high resolution environmental simulation. Unlike the vast majority of environmental modeling techniques that split the space into cells, the use of a vector space is proposed here. A phenomena will then be described by its shape, decomposed in several points that can move using a displacement vector. The shape also have a dynamic structure, as each point can instantiate new point because of a change in the space properties or to obtain a better resolution model. Such vector models are generating less overhead because the phenomena is recomputed only if a part of it is entering into a different space entity with different attributes, using cellular space the model would have been recomputed for each neighboring identical cells. This technique uses the DSDEVS formalism to describe discrete event models with dynamic structure, and will be implemented in the JDEVS toolkit also presented.

Tuesday 8:30:00 AM 10:00:00 AM
General Methodology 3

Chair: Don Dudenhoeffer (Idaho National Eng. and Env. Lab.)

A Parallel Simulation Framework for Infrastructure Modeling and Analysis
Donald D. Dudenhoeffer, May R. Permann, and Elliot M. Sussman (Idaho National Engineering & Environmental Laboratory)

Today's society relies greatly upon an array of complex national and international infrastructure networks, such as transportation, utilities, telecommunication, and even financial networks. While modeling and simulation tools have provided insight into the behavior of individual infrastructure networks, a far less understood area is that of the interrelationship between multiple networks. Specificially, how does and an event in one network affect the operation of other networks. This paper presents the work that is being conducted at the Idaho National Engineering and Environmental Laboratory (INEEL) to model and simulate these complex behaviors between coupled infrastructures.

Global Search Strategies for Simulation Optimisation
George D. Magoulas, Tillal Eldabi, and Ray J. Paul (Brunel University)

Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include add-on optimization tools. In this paper we are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncertainty that arises from stochastic elements of the process under study. We particularly investigate the use of global search methods in this context, as these methods allow the optimization strategy to escape from sub-optimal (i.e., local) solutions and, in that sense, they improve the efficiency of the simulation optimization process. The paper compares several global search methods and demonstrates the successful application of the Particle Swarm Optimizer to simulation modeling optimization and design of a steelworks plant, a representative example of the stochastic and unpredictable behavior of a complex discrete event simulation model.

A Federation Object Coordinator for Simulation based Control and Analysis
Seungyub Lee, Sreeram Ramakrishnan, and Richard A. Wysk (Pennsylvania State University)

This paper presents an architecture and a design for a Federation Object Coordinator (FOC) for simulation based control and analysis. This research focuses on developing a methodology for implementing a distributed simulation control mechanism which can be adopted to virtual manu-facturing or virtual enterprises. In this method, distributed fast or real time simulation models interact with low level controllers and among themselves to actively control a system. The timing and coordination requirements of the simulation models to interact with the MRP systems and control systems as well as the interaction among the dis-tributed simulation models are discussed in this paper.

Tuesday 10:30:00 AM 12:00:00 PM
General Applications 1

Chair: Hansoo Kim (Georgia Institute of Technology)

A Simulation Architecture with Distributed Controllers for Cell-Based Manufacturing Systems
Hansoo Kim, SugJe Sohn, Ying Wang, Tolga Tezcan, Leon McGinnis, and Chen Zhou (Georgia Institute of Technology)

A number of manufacturing systems are categorized into Cell-Based Manufacturing Systems (CBMSs), which have similar physical configurations typically composed of manufacturing cells and transporter controllers. However, since the control modules are not separable from the model in conventional simulation frameworks, it is difficult to develop a generic high-fidelity simulator for CBMSs with flexibility in control structure. We propose a generic simulation architecture for CBMSs, CellSim, using the concept of Controller-In-Loop (CiL), in which all supervisory controllers are separated from simulation models, and developed as individual controller modules. CellSim has three sub-architectures, i.e., the design/modeling architecture, the simulation execution architecture, and output analysis architecture. CellSim has distinctive features and advantages for flexible, high-fidelity, and integrated manufacturing system design and modeling, compared to the conventional simulation frameworks. In this paper, we present distinctive features and architecture of CellSim for CBMSs as well as the concept and implementation of CiL.

A Highly Efficient M/G/∞ Model for Generating Self-Similar Traces
María Estrella Sousa-Vieira, Andrés Suárez-González, Cándido López-García, Manuel Fernández-Veiga, and José Carlos López-Ardao (Universidade de Vigo)

Several traffic measurement reports have convincingly shown the presence of self-similarity in modern networks, inducing as a result a revolution in the stochastic modeling of traffic. The use of self-similar processes in performance analysis has opened new problems and research issues in simulation studies, where the efficient generation of synthetic sample paths with self-similar properties is one of the fundamental concerns. In this paper, we present an M/G/infinity generator of self-similar traces, based on a highly efficient simulation model using the decomposition property of Poisson processes.

Soccer Championship Analysis Using Monte Carlo Simulation
Caio Fiuza Silva, Eduardo Saggioro Garcia, and Eduardo Saliby (COPPEAD / UFRJ)

Sports had always fascinated humanity. In this context, soccer was taken as a study source. The objective of this paper is to formulate a simulation model to generate estimators for necessary scores to achieve certain places at the final classification ranking of the Brazilian National Soccer Championship. The main data used are the rules of the championship, the number of competitors and the probability that a match ends up in a draw.

Tuesday 1:30:00 PM 3:00:00 PM
General Applications 2

Chair: Guixiu Qiao (NIST)

Decision Making of Embedded I/O Buffer Sizes Using the Queueing Simulation Model for a Shared-Memory System
Jui-Hua Li, JoAnne Holliday, and George Fegan (Santa Clara University)

This paper presents a methodology of decision-making for embedded I/O buffer sizes in a single-bus, shared-memory system. The decision is made with the aid of a queuing model, simulation, and the proposed algorithm. The generalized queueing model is simulated to cover two cases: independent processing units and pipelined processing units in a shared-memory environment. The objective is to obtain the best performance with the optimized embedded buffers in the system. Therefore, an algorithm is developed to find the optimal solution efficiently by exploring the correlation between buffers and system performance. The local optimum is guaranteed. The method can be widely applied to many applications.

A General Simulation Environment for IP Mobility
Peng Sun and Sam Y. Sung (National University of Singapore)

This paper describes an advanced simulation environment that has been used to examine, validate, and predict the performance of Protocols for IP Mobility Support. It overcomes many limitations found in existing network simulators, and it provides more support on mobile-related issues. It contains several components that are common to all evaluations of IP mobility, which can model arbitrary network topologies, arbitrary movement pattern, and arbitrary calling patterns. It also provides a set of protocol implementations that are necessary to simulating the Internet. The environment offers several desirable features including: 1) flexible metrics collection for both predefined and customized ones, 2) reuse of calling patterns, moving patterns, network topologies, etc. and 3) automatic generation of mobility patterns. Several research contributions had been made with the help of this simulation environment, and it would be useful for refining various aspects of IP mobility support.

Simulation System Modeling for Mass Customization Manufacturing
Guixiu Qiao, Charles McLean, and Frank Riddick (NIST)

Emerging rapidly as a new paradigm of the 21st century, Mass Customization Manufacturing (MCM) systems possess some special characteristics that make the modeling of such systems extremely difficult. These characteristics include concurrency, synchronization, and cooperation among subsystems. Moreover, MCM emphasizes shortened product life-cycles, which means production lines have to be changed or reconfigured frequently. Highly flexible and re-configurable factories must be designed, simulated, and analyzed. To support the development and analysis of these systems, new approaches to modeling and simulation must be developed. In this paper, a methodology for representing manufacturing systems using valid, colored Petri Net is presented. This method for modeling and simulating is flexible enough to support the dynamic nature of the operation of MCM systems. It is able to represent solutions to problems such as dynamic rescheduling, shop reconfiguration, part rework processing, and mechanisms for recovery from machine failure

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