Discrete-event system simulation / Jerry Banks, John S. Carson, Barry L. Nelson.

TABLE OF CONTENTS

Part One: Introduction to Discrete-Event Systems Simulation

1 : Introduction to Simulation
1.1 When is Simulation the Appropriate Tool?
1.2 Advantages and Disadvantages of Simulation
1.3 Areas of Application
1.4 Systems and System Environment
1.5 Components of a System

2 : Simulation Examples
2.1 Simulation of Queueing Systems
2.2 Simulation of Inventory Systems
2.3 Other Examples of Simulation

3 : General Principles
3.1 Concepts in Discrete-Event Simulation
3.2 List Processing

4 : Programming Languages
4.1 Simulation in FORTRAN
4.2 Simulation in GPSS
4.3 Simulation in SIMAN V
4.4 Simulation in SIMSCRIPT 11.5
4.5 Simulation in SLAM II Using SLAMSYSTEM
Etc.

5 : Simulation of Manufacturing and Material Handling Systems
5.1 Manufacturing and Material Handling Systems
5.2 Goals and Performance Measures
5.3 Issues in Manufacturing and Material Handling Simulations
5.4 Case Studies of the Simulations of Manufacturing and Material Handling Systems
5.5 Simulators and Languages for Manufacturing and Material Handling

Part Two: Mathematical and Statistical Models

6 : Statistical Models in Simulation
6.1 Review of Terminology and Concepts
6.2 Useful Statistical Models
6.3 Discrete Distributions
6.4 Continuous Distributions
6.5 Poisson Process
Etc.

7 : Queueing Models
7.1 Characteristics of Queueing Models
7.2 Queueing Notation
7.3 Transient and Steady-State Behavior of Queues
7.4 Long-Run Measures of Performance of Queueing Systems
7.5 Steady-State Behavior of Infinite-Population Markovian Models
Etc.

Part 3: Random Numbers

8 : Random-Number Generation
8.1 Properties of Random Numbers
8.2 Generation of Pseudo-Random Numbers
8.3 Techniques for Generating Random Numbers
8.4 Tests for Random Numbers

9 : Random Variate Generation
9.1 Inverse Transform Technique
9.2 Direct Transformation for the Normal Distribution
9.3 Convolution Method
9.4 Acceptance-Rejection Technique

Part Four: Analysis of Simulation Data

10 : Input Modeling
10.1 Data Collection
10.2 Identifying the Distribution with Data
10.3 Parameter Estimation
10.4 Goodness-of -fit Tests
10.5 Selecting Input Models Without Data
Etc.

11 : Verification and Validation of Simulation Models
11.1 Model Building, Verification, and Validation
11.2 Verification of Simulation Models
11.3 Calibration and Validation of Models

12 : Output Analysis for a Single Model
12.1 Stochastic Nature of Output Data
12.2 Types of Simulations with Respect to Output Analysis
12.3 Measures of Performance and their Estimation
12.4 Output Analysis for Terminating Simulations
12.5 Output Analysis for Steady-State Simulations

13 : Comparison and Evaluation of Alternative System Design
13.1 Comparison of Two System Designs
13.2 Comparison of Several System Designs
13.3 Statistical Models for Estimating the Effect of Design Alternatives
13.4 Metamodeling

Includes bibliographical references and index: p. 543- 548