Chapter1 Basic Simulation Modeling

1.1 The Nature of Simulation

1.2 Systems,Models,and Simulation

1.3 Discrete-Event Simulation

1.3.1 Time-Advance Mechanisms

1.3.2 Components and Organization of a Discrete-event Simulation Model

1.4 Simulation of a Single-Server Queueing System

1.4.1 Problem Statement

List of Symbols

1.4.2 Intuitive Explanation

Preface

1.4.3 Program Organization and Logic

1.4.4 FORTRAN Program

1.4.5 C Program

1.4.6 Simulation Output and Discussion

1.4.7 Alternative Stopping Rules

1.4.8 Determining the Events and Variables

1.5.1 Problem Statement

1.5 Simulation of and Inventory System

1.5.2 Program Organization and Logic

1.5.3 FORTRAN Program

1.5.4 C Program

1.5.5 Simulation Output and Discussion

1.6 Alternative Approaches to Modeling and Coding Simulations

1.6.1 Parallel and Distributed Simulation

1.6.2 Simulation across the Internet and Web-Based Simulation

1.7 Steps in a Sound Simulation Study

1.8 Other Types of Simulation

1.8.1 Continuous Simulation

1.8.2 Combined Discrete-Continuous Simulation

1.8.3 monte Carlo Simulation

1.9 Advantages,Disadvantages,and Pitfalls of Simulation

Appendix 1A:Fixed-Increment Time Advance

Appendix 1B:A Primer on Queueing Systems

1B.2 Notation for Queueing Systems

1B.1 Components of a Queueing System

1B.3 Measures of Performance for Queueing Systems

Problems

Chapter2 Modeling Complex Systems

2.1 Introduction

2.2 List Processing in Simulation

2.2.1 Approaches to Storing Lists in a Computer

2.2.2 Linked Storage Allocation

2.3 A Simple Simulation Language:simlib

2.4.2 simlib Program

2.4 Single-Server Queueing Simulation with simlib

2.4.1 Problem Statement

2.4.3 Simulation Output and Discussion

2.5 Time-Shared Computer Model

2.5.1 Problem Statement

2.5.2 simlib Program

2.5.3 Simulation Output and Discussion

2.6.1 Problem Statement

2.6 Multiteller Bank with Jockeying

2.6.2 simlib Program

2.6.3 Simulation Output and Discussion

2.7 Job-Shop Model

2.7.1 Problem Statement

2.7.2 simlib Program

2.7.3 Simulation Output and Discussion

2.8 Efficient Event-List Manipulation

Appendix2A: C Code for simlib

Problems

Chapter3 Simulation Software

3.1 Introduction

3.2 Comparison of Simulation Packages with Programming Languages

3.3 Classification of Simulation Software

3.3.1 General-Purpose Versus Application-oriented Simulation Packages

3.3.2 Modeling Approaches

3.3.3 Common Modeling Elements

3.4.1 General Capabilities

3.4 Desirable Software Features

3.4.2 Hardware and Software Requirements

3.4.3 Animation and Dynamic Graphics

3.4.4 Statistical Capabilities

3.4.5 Customer Support and Documentation

3.4.6 Output Reports and Craphics

3.5 General-Purpose Simulation Packages

3.5.1 Arena

3.5.2 Extend

3.5.3 Other General-Purpose Simulation Packages

3.6 Object-Oriented Simulation

3.6.1 MODSIM III

3.7 Examples of Application-Oriented Simulation Packages

Chapter4 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

Appendix4A:Comments on Covariance-Stationary Processes

Problems

Chapter5 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

5.4.1 Collect High-Quality Information and Data on the System

5.4.2 Interact with the Manager on a Regular Basis

5.4.3 Maintain and Assumptions Document and Perform a Structured Walk-Through

5.4.4 Validate Components of the Model by Using Quantitative Techniques

5.4.5 Validate the Output from the Overall Simulation Model

5.4.6 Animation

5.5 Management s Role in the Simulation Process

5.6.1 Inspection Approach

5.6 Statistical Procedures for Comparing Real-World Observations and Simulation Output Data

5.6.2 Confidence-Interval Approach Based on Independent Data

5.6.3 Time-Series Approaches

Problems

Chapter6 Selecting Input Probability Distributions

6.1 Introduction

6.2 Useful Probability Distributions

6.2.1 Parameterization of Continuous Distributions

6.2.2 Continuous Distributions

6.2.3 Discrete Distributions

6.2.4 Empirical Distributions

6.3 Techniques for Assessing Sample Independence

6.4 Activity Ⅰ:Hypothesizing Families of Distributions

6.4.1 Summary Statistics

6.4.2 Histograms

6.4.3 Quantile Summaries and Box Plots

6.5 ActivityⅡ：Estimation of Parameters

6.6.1 Heuristic Procedures

6.6 ActivityⅢ:Determining How Representative the Fitted Distributions Are

6.6.2 Goodness-of-Fit Tests

6.7 The ExpertFit Software and an Extended Example

6.8 Shifted and Truncated Distributions

6.9 Bezier Distributions

6.10 Specifying Multivariate Distributions,Correlations,and Stochastic Processes

6.10.1 Specifying Multivariate Distributions

6.10.2 Specifying Arbitrary Marginal Distributions and Correlations

6.10.3 Specifying Stochastic Processes

6.11 Selecting a Distribution in the Absence of Data

6.12 Models of Arrival Processes

6.12.1 Poisson Processes

6.12.2 Nonstationary Poisson Processes

6.12.3 Batch Arrivals

6.13 Assessing the Homogeneity of Different Data Sets

Appendix 6A:Tables of MLEs for the Gamma and Beta Distributions

Problems

Chapter7 Random-Number Generators

7.1 Introduction

7.2 Linear Congruential Generators

7.2.1 Mixed Generators

7.2.2 Multiplicative Generators

7.3 Other Kinds of Generators

7.3.1 More General Congruences

7.3.2 Composite Generators

7.3.3 Tausworthe and Related Generators

7.4 Testing Random-Number Generators

7.4.1 Empirical Tests

7.4.2 Theoretical Tests

7.4.3 Some General Observations on Testing

Appendix7A:Portable Computer Codes for a PMMLCG

7A.1 FORTRAN

7A.2 C

7A.3 Obtaining Initial Seeds for the Streams

Appendix 7B:Portable C Code for a Combined MRG

Problems

8.1 Introduction

Chapter8 Generating Random Variates

8.2 General Approaches to Generating Random Variates

8.2.1 Inverse Transform

8.2.2 Composition

8.2.3 Convolution

8.2.4 Acceptance-Rejection

8.2.5 Special Properties

8.3 Generating Continuous Random Variates

8.3.1 Uniform

8.3.2 Exponential

8.3.3 m-Erlang

8.3.4 Gamma

8.3.5 Weibull

8.3.6 Normal

8.3.7 Lognormal

8.3.8 Beta

8.3.12 Johnson Bounded

8.3.11 Log-Logistic

8.3.9 Pearson Type V

8.3.10 Pearson Type VI

8.3.13 Johnson Unbounded

8.3.14 Bezier

8.3.15 Triangular

8.3.16 Empirical Distributions

8.4 Generating Discrete Random Variates

8.4.2 Discrete Uniform

8.4.3 Arbitrary Discrete Distribution

8.4.1 Bernoulli

8.4.4 Binonial

8.4.5 Geometric

8.4.6 Negative Binomial

8.4.7 Poisson

8.5 Generating Random Vectors,Correlated Random Variates,and Stochastic Processes

8.5.1 Using Conditional Distributions

8.5.2 Multivariate Normal and Multivariate Lognormal

8.5.3 Correlated Gamma Random Variates

8.5.5 Generating Random Vectors with Arbitrarily Specified Marginal Distributions and Correlations

8.5.4 Generating from Multivariate Families

8.5.6 Generating Stochastic Processes

8.6 Generating Arrival Processes

8.6.1 Poisson Processes

8.6.2 Nonstationary Poisson Processes

8.6.3 Batch Arrivals

Appendix8A:Validity of the Acceptance-Rejection Method

Appendix8B:Setup for the Alias Method

Problems

9.1 Introduction

Chapter9 Output Data Analysis for a Single System

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

9.4.1 Estimating Means

9.4.2 Estimating Other Measures of Performance

9.4.3 Choosing Initial Conditions

9.5 Statistical Analysis for Steady-State Parameters

9.5.1 The Problem of the Initial Transient

9.5.2 Replicfation/Daletion Approaches for Means

9.5.3 Other Approaches for Means

9.5.4 Estimating Other Measures of Performance

9.6 Statistical Analysis for Steady-State Cycle Parameters

9.7 Multiple Measures of Performance

9.8 Time Plots of Important Variables

Appendix9A:Ratios of Expectations and Jackknife Estimators

Problems

10.1 Introduction

Chapter10 Comparing Alternative System Configurations

10.2 Confidence Intervals for the Difference Between the Expected Responses of Two Systems

10.2.1 A Paired-t Confidence Interval

10.2.2 A Modified Two-Sample-t Confidence Interval

10.2.3 Contrasting the Two Methods

10.2.4 Comparisons Based on Steady-State Measures of Performance

10.3 Confidence Intervals for Comparing More than Two Systems

10.3.1 Comparisons with a Standard

10.3.2 All Pairwise Comparisons

10.4 Ranking and Selection

10.3.3 Multiple Comparisons with the Best

10.4.2 Selecting a Subset of Size m Containing the Best of k Systems

10.4.3 Selecting the m Best of k Systems

10.4.4 Additional Problems and Methods

Appendix 10A:Validity of the Selection Procedures

Appendix 10B:Constants for the Selection Procedures

Problems

Chapter11 Variance-Reduction Techniques

11.1 Introduction

11.2 Common Random Numbers

11.2.1 Rationale

11.2.2 Applicability

11.2.3 Synchronization

11.2.4 Some Examples

10.4.1 Selecting the Best of k Systems

11.3 Antithetic Variates

11.4 Control Variates

11.5 Indirect Estimation

11.6 Conditioning

Problems

Chapter12 Experimental Design,Sensitivity Analysis,and Optimization

12.1 Introduction

12.2 2k Factorial Designs

12.3 Coping with Many Factors

12.3.1 2k-p Fractional Factorial Designs

12.3.2 Factor-Screening Strategies

12.4 Response Surfaces and Metamodels

12.5 Sensitivity and Gradient Estimation

12.6 Optimum Seeking

12.6.1 Optimum-Seeking Methods

12.6.2 Optimum-Seeking Packages Interfaced with Simulation Software

Problems

Chapter13 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

13.4.1 Sources of Randomness

13.4.2 Machine Downtimes

13.5 An Extended Example

13.5.1 Problem Description and Simulation Results

13.5.2 Statistical Calculations

13.6.1 Description of the System

13.6 A Simulation Case Study of a Metal-Parts Manufacturing Facility

13.6.2 Overall Objectives and Issues to Be Investigated

13.6.3 Development of the Model

13.6.4 Model Verification and Validation

13.6.5 Results of the Simulation Experiments

13.6.6 Conclusions and Benefits

Problems

Appendix

References

Subject Index