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Summary
Summary
This volume introduces computational and mathematical techniques for modeling, simulating, and analyzing the performance of various systems. Helps readers gain a better understanding of how systems operate and respond to change by: 1) helping them begin to model, simulate, and analyze simple-but-representative systems as soon as possible; and 2) whenever possible, encouraging the experimental exploration and self-discovery of theoretical results before their formal presentation. Features an approachable writing style that emphasizes concepts and insight without sacrificing rigor. Provides C software as source code for running simulations developed in the book, eliminating the need for readers to do all their programming from scratch. Emphasizes an algorithmic approach throughout. A useful reference for industrial engineers.
Table of Contents
| 1 Models |
| 1.1 Introduction |
| 1.2 A Single-Server Queue |
| 1.3 A Simple Inventory System |
| 2 Random Number Generation |
| 2.1 Lehmer Random Number Generation: Introduction |
| 2.2 Lehmer Random Number Generation: Implementation |
| 2.3 Monte Carlo Simulation |
| 2.4 Monte Carlo Simulation Examples |
| 3 Discrete-Event Simulation |
| 3.1 Discrete-Event Simulation |
| 3.2 Multi-Stream Lehmer Random Number Generation |
| 3.3 Discrete-Event Simulation Models |
| 4 Statistics |
| 4.1 Sample Statistics |
| 4.2 Discrete-Data Histograms |
| 4.3 Continuous-Data Histograms |
| 4.4 Correlation |
| 5 Next-Event Simulation |
| 5.1 Next-Event Simulation |
| 5.2 Next-Event Simulation Examples |
| 5.3 Event List Management |
| 6 Discrete Random Variables |
| 6.1 Discrete Random Variables |
| 6.2 Generating Discrete Random Variables |
| 6.3 Discrete Random Variable Applications |
| 6.4 Discrete Random Variable Models |
| 6.5 Random Sampling |
| 7 Continuous Random Variables |
| 7.1 Continuous Random Variables |
| 7.2 Generating Continuous Random Variables |
| 7.3 Continuous Random Variable Applications |
| 7.4 Continuous Random Variable Models |
| 7.5 Nonstationary Poisson Processes |
| 7.6 Acceptance-Rejection |
| 8 Input Modeling |
| 8.1 Error in Discrete-Event Simulation |
| 8.2 Modeling Stationary Processes |
| 8.3 Modeling Nonstationary Processes |
| 9 Output Analysis |
| 9.1 Interval Estimation |
| 9.2 Monte Carlo Estimation |
| 9.3 Finite-Horizon and Infinite-Horizon Statistics |
| 9.4 Batch Means |
| 9.5 Steady-State Single-Server Service Node Statistics |
| 10 Projects |
| 10.1 Empirical Tests of Randomness |
| 10.2 Birth-Death Processes |
| 10.3 Finite-State Markov Chains |
| 10.4 A Network of Single-Server Service Nodes |
| Appendices |
| A Simulation Languages |
| B Integer Arithmetic |
| C Parameter Estimation Summary |
| D Random Variate Models |
| E Random Variate Generators |
