Proceedings of the 2006 Winter Simulation Conference

3-6 Dec. 2006

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  • WSC Sponsoring Organizations

    Publication Year: 2006, Page(s): xxviii
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  • WSC Board of Directors

    Publication Year: 2006, Page(s): xxix
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  • WSC 2006 Conference Committee

    Publication Year: 2006, Page(s): xxx
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  • WSC `06 Program Structure and Track Coordinators

    Publication Year: 2006, Page(s):xxxi - xxxiii
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  • Referees

    Publication Year: 2006, Page(s):xxxiv - xxxix
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  • The Winter Simulation Conferences

    Publication Year: 2006, Page(s):xl - xliii
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  • WSC Foundation

    Publication Year: 2006, Page(s): xliv
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  • Author index

    Publication Year: 2006, Page(s):nil1 - nil16
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  • Announcement for WSC '07

    Publication Year: 2006, Page(s):nil17 - nil19
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  • Author directory

    Publication Year: 2006, Page(s):2309 - 2369
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  • Preface

    Publication Year: 2006, Page(s):xxv - xxvii
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  • Proceedings of the 2006 Winter Simulation Conference

    Publication Year: 2006, Page(s): i
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  • Table of contents

    Publication Year: 2006, Page(s):iii - xxiv
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  • White noise' assumptions revisited: Regression metamodels & experimental design in practice

    Publication Year: 2006, Page(s):1 - 4
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  • Discrete Event Models: Getting the Semantics Right

    Publication Year: 2006, Page(s): 1
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (35 KB) | HTML iconHTML

    Discrete event models are systems where components interact via timed events. Although there are many languages and simulators with discrete-event semantics (VHDL, OpNet Modeler, ns2, etc.), there is not widespread agreement on the precise semantics. This talk examines a formal foundation for discrete-event systems that clarifies interesting corner cases, where events are simultaneous, ordered or ... View full abstract»

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  • As Simple As Possible, but No Simpler: A Gentle Introduction to Simulation Modeling

    Publication Year: 2006, Page(s):2 - 10
    Cited by:  Papers (11)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (9660 KB) | HTML iconHTML

    We start with basic terminology and concepts of modeling, and decompose the art of modeling as a process. This overview of the process helps clarify when we should or should not use simulation models. We discuss some common missteps made by many inexperienced modelers, and propose a concrete approach for avoiding those mistakes. After a quick review of event graphs, which are a very straightforwar... View full abstract»

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  • Spreadsheet Simulation

    Publication Year: 2006, Page(s):11 - 18
    Cited by:  Papers (7)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (225 KB) | HTML iconHTML

    "Spreadsheet simulation" refers to the use of a spreadsheet as a platform for representing simulation models and performing simulation experiments. This tutorial explains the reasons for using this platform for simulation, discusses why this is frequently an efficient way to build simulation models and execute them, describes how to setup a spreadsheet simulation, and finally examines some limitat... View full abstract»

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  • Introduction to Modeling and Generating Probabilistic Input Processes for Simulation

    Publication Year: 2006, Page(s):19 - 35
    Cited by:  Papers (5)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (371 KB) | HTML iconHTML

    Techniques are presented for modeling and generating the univariate and multivariate probabilistic input processes that drive many simulation experiments. Among univariate input models, emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Among bivariate and higher-dimensional input models, emphasis is g... View full abstract»

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  • Output Analysis for Simulations

    Publication Year: 2006, Page(s):36 - 46
    Cited by:  Papers (10)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (178 KB) | HTML iconHTML

    We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Terminating and steady-state simulations are considered View full abstract»

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  • Work Smarter, not Harder: Guidelines for Designing Simulation Experiments

    Publication Year: 2006, Page(s):47 - 57
    Cited by:  Papers (10)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (360 KB) | HTML iconHTML

    We present the basic concepts of experimental design, the types of goals it can address, and why it is such an important and useful tool for simulation. A well-designed experiment allows the analyst to examine many more factors than would otherwise be possible, while providing insights that cannot be gleaned from trial-and-error approaches or by sampling factors one at a time. We focus on experime... View full abstract»

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  • How to Build Valid and Credible Simulation Models

    Publication Year: 2006, Page(s):58 - 66
    Cited by:  Papers (14)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (187 KB) | HTML iconHTML

    In this tutorial we present techniques for building valid and credible simulation models. Ideas to be discussed include the importance of a definitive problem formulation, discussions with subject-matter experts, interacting with the decision-maker on a regular basis, development of a written assumptions document, structured walk-through of the assumptions document, use of sensitivity analysis to ... View full abstract»

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  • Tips for the Successful Practice of Simulation

    Publication Year: 2006, Page(s):67 - 72
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    Succeeding with a technology as powerful as simulation involves much more than the technical aspects you may have been trained in. The parts of a simulation study that are outside the realm of modeling and analysis can make or break the project. This paper explores the most common pitfalls in performing simulation studies and identifies approaches for avoiding these problems View full abstract»

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  • Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents

    Publication Year: 2006, Page(s):73 - 83
    Cited by:  Papers (90)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (598 KB) | HTML iconHTML

    Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of interacting autonomous agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to do research. Some have gone so far as to contend that ABMS is a new way of doing science. Computational advances make... View full abstract»

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  • Parallel and Distributed Simulation: Traditional Techniques and Recent Advances

    Publication Year: 2006, Page(s):84 - 95
    Cited by:  Papers (42)  |  Patents (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (266 KB) | HTML iconHTML

    This tutorial on parallel and distributed simulation systems reviews some of the traditional synchronization techniques and presents some recent advances View full abstract»

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  • Bayesian Ideas and Discrete Event Simulation: Why, What and How

    Publication Year: 2006, Page(s):96 - 106
    Cited by:  Papers (16)  |  Patents (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (199 KB) | HTML iconHTML

    Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the performance of systems, or about inputs parameters of those systems. They also can be used to improve t... View full abstract»

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