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 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.