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Table of ContentsTable of Contents
(55.0K)
List of Symbols
Preface
Chapter 1 Basic Simulation Modeling
1.1 The Nature of Simulation
1.2 Systems, Models, and Simulation
1.3 Discrete-Event Simulation
1.4 Simulation of a Single-Server Queueing System
1.5 Simulation of an Inventory System
1.6 Parallel/Distributed Simulation and the High Level Architecture
1.7 Steps in a Sound Simulation Study
1.8 Other Types of Simulation
1.9 Advantages, Disadvantages, and Pitfalls of Simulation
Appendix 1A: Fixed-Increment Time Advance
Appendix 1B: A Primer on Queueing Systems
Chapter 2 Modeling Complex Systems
2.1 Introduction
2.2 List Processing in Simulation
2.3 A Simple Simulation Language: simlib
2.4 Single-Server Queueing Simulation with simlib
2.5 Time-Shared Computer Model
2.6 Multiteller Bank With Jockeying
2.7 Job-Shop Model
2.8 Efficient Event-List Manipulation
Appendix 2A: C Code for simlib
Chapter 3 Simulation Software
3.1 Introduction
3.2 Comparison of Simulation Packages with Programming
Languages
3.3 Classification of Simulation Software
3.4 Desirable Software Features
3.5 General-Purpose Simulation Packages
3.6 Object-Oriented Simulation
3.7 Examples of Application-Oriented Simulation Packages
Chapter 4 Review of Basic Probability and Statistics
4.1 Introduction
4.2 Random Variables and Their Properties
4.3 Simulation Output Data and Stochastic Processes
4.4 Estimation of Means, Variances, and Correlations
4.5 Confidence Intervals and Hypothesis Tests for the Mean
4.6 The Strong Law of Large Numbers
4.7 The Danger of Replacing a Probability Distribution by
its Mean
Appendix 4A: Comments on Covariance-Stationary Processes
Chapter 5 Building Valid, Credible, and Appropriately Detailed
Simulation Models
5.1 Introduction and Definitions
5.2 Guidelines for Determining the Level of Model Detail
5.3 Verification of Simulation Computer Programs
5.4 Techniques for Increasing Model Validity and Credibility
5.5 Management’s Role in the Simulation Process
5.6 Statistical Procedures for Comparing Real-World Observations and Simulation Output Data
Chapter 6 Selecting Input Probability Distributions
6.1 Introduction
6.2 Useful Probability Distributions
6.3 Techniques for Assessing Sample Independence
6.4 Activity I: Hypothesizing Families of Distributions
6.5 Activity II: Estimation of Parameters
6.6 Activity III: Determining How Representative
the Fitted Distributions Are
6.7 The ExpertFit Software and an Extended Example
6.8 Shifted and Truncated Distributions
6.9 Bézier Distributions
6.10 Specifying Multivariate Distributions, Correlations,
and Stochastic Processes
6.11 Selecting a Distribution in the Absence of Data
6.12 Models of Arrival Processes
6.13 Assessing the Homogeneity of Different Data Sets
Appendix 6A: Tables of MLEs for the Gamma and Beta Distributions
Chapter 7 Random-Number Generators
7.1 Introduction
7.2 Linear Congruential Generators
7.3 Other Kinds of Generators
7.4 Testing Random-Number Generators
Appendix 7A: Portable C Code for a PMMLCG
Appendix 7B: Portable C Code for a Combined MRG
Chapter 8 Generating Random Variates
8.1 Introduction
8.2 General Approaches to Generating Random Variates
8.3 Generating Continuous Random Variates
8.4 Generating Discrete Random Variates
8.5 Generating Random Vectors, Correlated Random Variates,
and Stochastic Processes
8.6 Generating Arrival Processes
Appendix 8A: Validity of the Acceptance-Rejection
Method
Appendix 8B: Setup for the Alias Method
Chapter 9 Output Data Analysis for a Single System
9.1 Introduction
9.2 Transient and Steady-State Behavior of a Stochastic Process
9.3 Types of Simulations with Regard to Output Analysis
9.4 Statistical Analysis for Terminating Simulations
9.5 Statistical Analysis for Steady-State Parameters
9.6 Statistical Analysis for Steady-State Cycle Parameters
9.7 Multiple Measures of Performance
9.8 Time Plots of Important Variables
Appendix 9A: Ratios of Expectations and Jackknife
Estimators
Chapter 10 Comparing Alternative System Configurations
10.1 Introduction
10.2 Confidence Intervals for the Difference Between the
Expected Responses of Two Systems
10.3 Confidence Intervals for Comparing More than
Two Systems
10.4 Ranking and Selection
Appendix 10A: Validity of the Selection Procedures
Appendix 10B: Constants for the Selection Procedures
Chapter 11 Variance-Reduction Techniques
11.1 Introduction
11.2 Common Random Numbers
11.3 Antithetic Variates
11.4 Control Variates
11.5 Indirect Estimation
11.6 Conditioning
Chapter 12 Experimental Design and Optimization
12.1 Introduction
12.2 2k Factorial Designs
12.3 2k-p Fractional Factorial Designs
12.4 Response Surfaces and Metamodels
12.5 Simulation-Based Optimization
Chapter 13 Simulation of Manufacturing Systems
13.1 Introduction
13.2 Objectives of Simulation in Manufacturing
13.3 Simulation Software for Manufacturing
Applications
13.4 Modeling System Randomness
13.5 An Extended Example
13.6 A Simulation Case Study of a Metal-Parts Manufacturing
Facility
Appendix
References
Subject Index
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