Jerry Banks, Georgia Institute of Technology
John Carson, AutoSimulations, Inc.
Barry Nelson, Northwestern University

Published September, 1995 by Prentice Hall Engineering/Science/Mathematics

Copyright 1996, 548 pp.
Cloth
ISBN 0-13-217449-9

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    System Simulation-Industrial Engineering

    Simulation and Modeling-Computer Science

This text provides a basic treatment of discrete-event simulation, one of the most widely used operations research tools presently available. Proper collection and analysis of data, use of analytic techniques, verification and validation of models, and an 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. TheSecond Edition reorganizes, updates and expands coverage to reflect the most recent developments.




NEW—offers enhanced or totally new coverage spanning a wide range of topics, including:

• new examples of simulation applications, new interpretation of advantages and disadvantages of simulation, philosophical changes in the steps of a simulation study—Chapter 1.
• the concept of linked lists and other new material—Chapter 3.
• recent advances in simulation software, updated versions of simulation languages, new material on object-oriented simulation—Chapter 4.
• a new chapter that recognizes the concern for simulations of manufacturing and material handling systems and incorporates special purpose software—Chapter 5.
• new material on the M/G/oo queue, an approximation for the M/G/c queue, discussion of queue networks—Chapter 7.
• added a description of combined linear-congruential generators—Chapter 8.
• new material on approximating the inverse cdf—Chapter 9.
• discussions of the physical basis for various distributions for use as a guide to selecting candidate distributions, q-q plots for evaluating distribution fit to data, input modeling in the absence of data, a section on input models for multivariate processes—Chapter 10.
• recent material on verification and validation of simulation models—Chapter 11.
• new material on quantile estimation, in addition to mean and probability estimation. More emphasis on the important distinction between within-replication and across-replication analysis, enhanced treatment of the batch-means method—Chapter 12.
• a new discussion of sample size determination for comparison experiments, a ranking and selection procedure, a section on metamodeling—Chapter 13.

contains a wealth of examples, figures and tables to help readers assimilate newly learned concepts.



I. INTRODUCTION TO DISCRETE-EVENT SYSTEM SIMULATION.

1. Introduction to Simulation.
2. Simulation Examples.
3. General Principles.
4. Programming Languages.
5. Simulation of Manufacturing and Material Handling Systems.

II. MATHEMATICAL AND STATISTICAL MODELS.

6. Statistical Models in Simulation.
7. Queuing Models.

III. RANDOM NUMBERS.

8. Random-Number Generation.
9. Random Variate Generation.

IV. ANALYSIS OF SIMULATION DATA.

10. Input Modeling.
11. Verification and Validation of Simulation Models.
12. Output Analysis for a Single Model.
13. Comparison and Evaluation of Alternative System Designs.
Appendix A.
Index.

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