Discrete-Event System Simulation / Edition 2

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Overview

This book provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. Includes simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software. Covers properties, modeling and random-variate generation from the lognormal distribution. Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. For readers wanting to learn more about system simulation.
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Editorial Reviews

Booknews
A textbook for an introductory simulation course in engineering, computer science, or management or for a second course in simulation with a companion language text. The level is said to be the same as that of the second edition, but not otherwise specified. The only date noted is 1997, which could be the first or second. A fourth author, David M. Nicol (Dartmouth College), has jointed Barry L. Nelson (Northwest U.) and Banks and Johns S. Carson, both with an automation company. They have added a chapter on the modeling of computer systems and a website for the book in addition to updating the material generally. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Meet the Author

Jerry Banks retired in 1999 as a professor in the School of Industrial and Systems Engineering,

Georgia Institute of Technology, after which he worked as senior simulation technology advisor for Brooks Automation; he is currently a professor at Techno´ogico de Monterrey, M´exico. He is the author, coauthor, editor, or coeditor of twelve books, one set of proceedings, several chapters in texts, and numerous technical papers. His most recent book is RIFD Applied, co-authored with three others, and published by John Wiley in 2007. He is the editor of the Handbook of Simulation, published in 1998 by John Wiley, which won the award for Excellence in Engineering Handbooks from the Professional Scholarly Publishing Division of the Association of American Publishers, Inc. He is also author or coauthor of Getting Started with AutoMod, Second Edition, Introduction to SIMAN V and CINEMA V, Getting Started with GPSS/H, Second Edition, Forecasting and Management of Technology, Second Edition (in preparation) and Principles of Quality Control. He was a founding partner in the simulation-consulting firm Carson/Banks &Associates, Inc., which was purchased by AutoSimulations, Inc. He is a full member of many technical societies, among them the Institute of Industrial Engineers (IIE); he served eight years as that organization’s representative to the Board of the Winter Simulation Conference, including two years as board chair. He is the recipient of the INFORMS College on Simulation Distinguished Service Award for 1999 and was named a Fellow of IIE in 2002. John S. Carson II is an independent simulation consultant. Formerly, he held management and consulting positions in the simulation services and software industry, including positions atAutoSimulations and the AutoMod Group at Brooks Automation. He was the co-founder and president of the simulation services firm Carson/Banks &Associates. He has over 30 years experience in simulation in a wide range of application areas, including manufacturing, distribution, warehousing and material handling, order fulfillment systems, postal systems, transportation and rapid transit systems, port operations (container terminals and bulk handling), and health-care systems. He has taught simulation and operations research at the Georgia Institute of Technology and the University of Florida.

Barry L. Nelson is the Charles Deering McCormick Professor and Chair of the Department of Industrial Engineering and Management Sciences at Northwestern University. His research centers on the design and analysis of computer simulation experiments on models of stochastic systems, concentrating on multivariate input modeling and output analysis, optimization via simulation and metamodeling. Application areas include financial engineering, computer performance modeling, quality control, manufacturing and transportation systems. He is the Editor in Chief of Naval Research Logistics, a Fellow of INFORMS, and was simulation area editor of Operations Research, president of the INFORMS (then TIMS) College on Simulation, and Chair of the Board of Directors of the Winter Simulation Conference.

David M. Nicol is professor of electrical and computer engineering at the University of Illinois at Urbana-Champaign. He is a long-time contributor in the field of parallel and distributed discrete-event simulations, having written one of the early Ph.D. dissertations on the topic. He has also worked in parallel algorithms, algorithms for mapping workload in parallel architectures, performance analysis, and reliability modeling and analysis. His research contributions extend to 180 articles in leading computer-science journals and conferences. His research is driven largely by problems encountered in industry and government–he has worked closely with researchers at NASA, IBM,AT&T, Bellcore, Motorola, and the Los Alamos, Sandia, and Oak Ridge National Laboratories, as well as a number of aerospace and communication companies. His current interests lie in modeling and simulation of very large systems, particularly communications and other infrastructure, with applications in evaluating system security. From 1997 to 2003 he was the editor-in-chief of the ACM Transactions on Modeling and Computer Simulation. Professor Nicol is a Fellow of the IEEE, a Fellow of the ACM, and the inaugural awardee of the ACM SIGSIM Distinguished Contributions award.

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Table of Contents

(NOTE: Each chapter concludes with Summary, References, and Exercises.)

I. INTRODUCTION TO DISCRETE-EVENT SYSTEM SIMULATION.

1. Introduction to Simulation.
When Simulation Is the Appropriate Tool. When Simulation Is Not Appropriate. Advantages and Disadvantages of Simulation. Areas of Application. Systems and System Environment. Components of a System. Discrete and Continuous Systems. Model of a System. Types of Models. Discrete-Event System Simulation. Steps in a Simulation Study.

2. Simulation Examples.
Simulation of Queueing Systems. Simulation of Inventory Systems. Other Examples of Simulation.

3. General Principles.
Concepts in Discrete-Event Simulation. List Processing.

4. Simulation Software.
History of Simulation Software. Selection of Simulation Software. An Example Simulation. Simulation in C++. Simulation in GPSS. Simulation in CSIM. Simulation Packages. Experimentation and Statistical Analysis Tools. Trends in Simulation Software.

II. MATHEMATICAL AND STATISTICAL MODELS.

5. Statistical Models in Simulation.
Review of Terminology and Concepts. Useful Statistical Models. Discrete Distributions. Continuous Distributions. Poisson Process. Empirical Distributions.

6. Queueing Models.
Characteristics of QueueingSystems. Queueing Notation. Long-Run Measures of Performance of Queueing Systems. Steady-State Behavior of Infinite-Population Markovian Models. Steady-State Behavior of Finite-Population Models. Networks of Queues.

III. RANDOM NUMBERS.

7. Random-Number Generation.
Properties of Random Numbers. Generation of Pseudo-Random Numbers. Techniques for Generating Random Numbers. Tests for Random Numbers.

8. Random-Variate Generation.
Inverse Transform Technique. Direct Transformation for the Normal and Lognormal Distributions. Convolution Method. Acceptance-Rejection Technique.

IV. ANALYSIS OF SIMULATION DATA.

9. Input Modeling.
Data Collection. Identifying the Distribution with Data. Parameter Estimation. Goodness-of-Fit Tests. Selecting Input Models without Data. Multivariate and Time-Series Input Models.

10. Verification and Validation of Simulation Models.
Model Building, Verification, and Validation. Verification of Simulation Models. Calibration and Validation of Models.

11. Output Analysis for a Single Model.
Types of Simulations with Respect to Output Analysis. Stochastic Nature of Output Data. Measures of Performance and Their Estimation. Output Analysis for Terminating Simulations. Output Analysis for Steady-State Simulations.

12. Comparison and Evaluation of Alternative System Designs.
Comparison of Two System Designs. Comparison of Several System Designs. Metamodeling. Optimization via Simulation.

13. Simulation of Manufacturing and Material Handling Systems.
Manufacturing and Material Handling Simulations. Goals and Performance Measures. Issues in Manufacturing and Material Handling Simulations. Case Studies of the Simulation of Manufacturing and Material Handling Systems.

14. Simulation of Computer Systems.
Introduction. Simulation Tools. Model Input. High-Level Computer-System Simulation. CPU Simulation. Memory Simulation.

Appendix Tables.
Random Digits. Random Normal Numbers. Cumulative Normal Distribution. Cumulative Poisson Distribution. Percentage Points of the Students t Distribution with v Degrees of Freedom. Percentage Points of the Chi-Square Distribution with v Degrees of Freedom. Percentage Points of the F Distribution with …a = 0.05. Kolmogorov-Smirnov Critical Values. Maximum-Likelihood Estimates of the Gamma Distribution. Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n. Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n.

Index.
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