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# Discrete-Event System Simulation - 3rd edition

## ISBN13: 978-0130887023

ISBN13: 978-0130887023
ISBN10: 0130887021
Edition: 3RD 01
Publisher: Prentice Hall, Inc.
Published: 2001
International: No

# Discrete-Event System Simulation - 3RD 01 edition

## ISBN13: 978-0130887023

ISBN13: 978-0130887023
ISBN10: 0130887021
Edition: 3RD 01
Publisher: Prentice Hall, Inc.
Published: 2001
International: No
Summary

Appropriate for junior- senior-level Simulation courses in departments of Engineering, Management and Computer Science; or a second course in Simulation.

This text 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. The Third Edition reorganizes, updates and expands coverage to reflect the most recent developments in software and methodology, and adds a chapter on the simulation of computer systems. top

Features

• NEW--New sections on when simulation is the appropriate tool and not the appropriate tool to use and the future of simulation software.
• NEW--Updated material on the properties and operation of current simulation software--Including simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software.
• NEW--Addition of properties, modeling and random-variate generation from the lognormal distribution.
• NEW--Enhanced discussion of p-values and ''best fits''-- as used in input modeling software, and of input modeling without data.
• NEW--Greatly reorganized discussion of output analysis--Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics.
• NEW--Up-to-date treatment of simulation of manufacturing and material handling systems--Independent of the simulation packages used to simulate them.
• NEW--New chapter that focuses on how discrete-event simulation is used in the design and evaluation of computer systems--Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. Topics in model representation and model input are considered, as are examples of simulating a web-server system, a CPU that executes instructions out-of-order, and memory hierarchies.
• Simulation-software independent treatment of discrete-event simulation.
• Comprehensive coverage--Including modeling and analysis, simulation software, conducting successful simulation studies, and manufacturing, production and computer systems applications.
• Accessible to a wide audience at the undergraduate level.

Author Bio

Banks, Jerry : AutoSimulations, a Brooks Automation Company

Jerry Banks is Senior Simulation Technology Advisor, AutoSimulations, a Brooks Automation company, in their Atlanta, Georgia Office. He retired in June, 1999 as Professor, School of Industrial and Systems Engineering, Georgia Institute of Technology. He is the author, co-author, or co-editor of ten books, several chapters in texts, and numerous technical papers. He is the editor of the award winning {\it Handbook of Simulation}, published in 1998 by John Wiley. His latest title, {\it Getting Started with AutoMod}, was published by AutoSimulations in 2000. He was a founding partner in the simulation consulting firm Carson/Banks \& Associates, Inc. located in Atlanta. The firm was purchased by AutoSimulations, Inc.\ in May of 1994. He served eight years as the IIE representative to the Board of the Winter Simulation Conference, including two years as Board Chair. He was the recipient of the INFORMS College on Simulation Distinguished Service Award for 1999

Carson, John S. II : AutoSimulations, a Brooks Automation Company

John S. Carson II is the East Coast Consulting Manager for AutoSimulations, a Brooks Automation company. With AutoSimulations since 1994, he has over 22 years experience in simulation in a wide range of application areas, including manufacturing, distribution, warehousing and material handling, transportation and rapid transit systems, port operations and shipping, and medical/health care systems. His current interests center on the simulation of transportation systems, train systems, bulk and liquid processing, and user interface development. He has been an independent simulation consultant, and has taught at the Georgia Institute of Technology, the University of Florida, and the University of Wisconsin-Madison.

Nelson, Barry L. : Northwestern University

Barry L. Nelson is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University, and is Director of the Master of Engineering Management Program there. 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 problems. He has published numerous papers and two books. He has served as the Simulation Area Editor of {\it Operations Research\/} and President of the INFORMS (then TIMS) College on Simulation, and has held many positions for the annual Winter Simulation Conference, including Program Chair in 1997 and Board Member currently.

Nicol, David M. : Dartmouth College

David M. Nicol is Professor and chair of the Department of Computer Science at Dartmouth College. He is a long time contributor in the field of parallel and distributed discrete-event simulations, having written one of the early Ph.D. theses 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 well over 100 articles in leading computer science journals and conferences. His research is largely driven by problems encountered in industry and government---he has worked closely with researchers at NASA, IBM, AT&T, Bellcore, and Sandia National Laboratories. His current interests lie in modeling and simulation of very large systems, particularly communications and other infrastructure, with applications in evaluating system security. He has served on the editorial board of ACM Transactions on Modeling and Computer Simulation, where he is presently Editor-in-Chief.

(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 Queueing Systems. 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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