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.
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Für höhere Schule und Studium
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Höhe: 236 mm
Breite: 158 mm
Dicke: 26 mm
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ISBN-13
978-0-13-088702-3 (9780130887023)
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Schweitzer Klassifikation
(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.
