Discrete-Event System Simulation

by Banks, Jerry; Carson, John S.; Nelson, Barry L.; Nicol, David M.

• ISBN13: 9780130887023
• ISBN10: 0130887021
• Edition: 3rd
• Format: Hardcover
• Copyright: 2001-01-01
• Publisher: Prentice Hall

Summary

This book provides a basic treatment of discrete-event simulation, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Includes numerous solved examples. Offers an integrated website. Explains how to interpret simulation software output. For those interested in learning more about discrete-event simulation.

Table of Contents

Preface	xi
About the Authors	xiii
PART ONE: Introduction to Discrete-Event System Simulation
Introduction to Simulation	3	(20)
When Simulation Is the Appropriate Tool	4	(1)
When Simulation Is Not Appropriate	5	(1)
Advantages and Disadvantages of Simulation	6	(1)
Areas of Application	7	(2)
Systems and System Environment	9	(1)
Components of a System	10	(2)
Discrete and Continuous Systems	12	(1)
Model of a System	13	(1)
Types of Models	13	(1)
Discrete-Event System Simulation	14	(1)
Steps in a Simulation Study	15	(8)
References	20	(1)
Exercises	21	(2)
Simulation Examples	23	(40)
Simulation of Queueing Systems	24	(17)
Simulation of Inventory Systems	41	(6)
Other Examples of Simulation	47	(8)
Summary	55	(8)
References	55	(1)
Exercises	56	(7)
General Principles	63	(32)
Concepts in Discrete-Event Simulation	64	(21)
The Event-Scheduling/Time-Advance Algorithm	67	(5)
World Views	72	(3)
Manual Simulation Using Event Scheduling	75	(10)
List Processing	85	(7)
Lists: Basic Properties and Operations	86	(1)
Using Arrays for List Processing	87	(3)
Using Dynamic Allocation and Linked Lists	90	(2)
Advanced Techniques	92	(1)
Summary	92	(3)
References	93	(1)
Exercises	93	(2)
Simulation Software	95	(58)
History of Simulation Software	96	(4)
The Period of Search (1955--60)	97	(1)
The Advent (1961--65)	97	(1)
The Formative Period (1966--70)	98	(1)
The Expansion Period (1971--78)	98	(1)
Consolidation and Regeneration (1979--86)	99	(1)
The Present Period (1987--present)	99	(1)
Selection of Simulation Software	100	(4)
An Example Simulation	104	(1)
Simulation in C++	104	(10)
Simulation in GPSS	114	(5)
Simulation in CSIM	119	(4)
Simulation Packages	123	(6)
Arena	123	(1)
AutoMod	124	(1)
Deneb/QUEST	125	(1)
Extend	126	(1)
Micro Saint	127	(1)
ProModel	127	(1)
Taylor ED	128	(1)
WITNESS	128	(1)
Experimentation and Statistical Analysis Tools	129	(2)
Common Features	129	(1)
Analysis Tools	129	(2)
Trends in Simulation Software	131	(22)
High-Fidelity Simulation	131	(1)
Data Exchange Standards	132	(1)
The Internet	132	(1)
Old Paradigm versus New Paradigm	133	(1)
Component Libraries	133	(1)
Distributed Manufacturing Simulation/High Level Architeture	133	(1)
Embedded Simulation	134	(1)
Optimization	134	(1)
References	134	(2)
Exercises	136	(17)
PART TWO: Mathematical and Statistical Models
Statistical Models in Simulation	153	(51)
Review of Terminology and Concepts	154	(6)
Useful Statistical Models	160	(5)
Discrete Distributions	165	(5)
Continuous Distributions	170	(20)
Poisson Process	190	(3)
Empirical Distributions	193	(3)
Summary	196	(8)
References	196	(1)
Exercises	197	(7)
Queueing Models	204	(51)
Characteristics of Queueing Systems	205	(6)
The Calling Population	206	(1)
System Capacity	207	(1)
The Arrival Process	207	(2)
Queue Behavior and Queue Discipline	209	(1)
Service Times and the Service Mechanism	209	(2)
Queueing Notation	211	(1)
Long-Run Measures of Performance of Queueing Systems	212	(12)
Time-Average Number in System L	213	(2)
Average Time Spent in System per Customer, ω	215	(1)
The Conservation Equation: L = λω	216	(2)
Server Utilization	218	(5)
Costs in Queueing Problems	223	(1)
Steady-State Behavior of Infinite-Population Markovian Models	224	(15)
Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1	225	(6)
Multiserver Queue: M/M/c/∞/∞	231	(6)
Multiserver-Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/∞	237	(2)
Steady-State Behavior of Finite-Population Models (M/M/c/K/K)	239	(4)
Networks of Queues	243	(2)
Summary	245	(10)
References	246	(1)
Exercises	247	(8)
PART THREE: Random Numbers
Random-Number Generation	255	(34)
Properties of Random Numbers	255	(1)
Generation of Pseudo-Random Numbers	256	(2)
Techniques for Generating Random Numbers	258	(6)
Linear Congruential Method	258	(4)
Combined Linear Congruential Generators	262	(2)
Tests for Random Numbers	264	(20)
Frequency Tests	266	(4)
Runs Tests	270	(8)
Tests for Autocorrelation	278	(3)
Gap Test	281	(2)
Poker Test	283	(1)
Summary	284	(5)
References	284	(1)
Exercises	285	(4)
Random-Variate Generation	289	(34)
Inverse Transform Technique	290	(17)
Exponential Distribution	290	(4)
Uniform Distribution	294	(1)
Weibull Distribution	294	(1)
Triangular Distribution	295	(1)
Empirical Continuous Distributions	296	(4)
Continuous Distributions without a Closed-Form Inverse	300	(1)
Discrete Distributions	301	(6)
Direct Transformation for the Normal and Lognormal Distributions	307	(2)
Convolution Method	309	(1)
Erland Distribution	309	(1)
Acceptance-Rejection Technique	310	(5)
Poisson Distribution	311	(3)
Gamma Distribution	314	(1)
Summary	315	(8)
References	316	(1)
Exercises	316	(7)
PART FOUR: Analysis of Simulation Data
Input Modeling	323	(44)
Data Collection	324	(3)
Indentifying the Distribution with Data	327	(9)
Histograms	327	(4)
Selecting the Family of Distributions	331	(2)
Quantile-Quantile Plots	333	(3)
Parameter Estimation	336	(7)
Preliminary Statistics: Sample Mean and Sample Variance	336	(2)
Suggested Estimators	338	(5)
Goodness-of-Fit Tests	343	(8)
Chi-Square Test	343	(3)
Chi-Square Test with Equal Probabilities	346	(2)
Kolmogorov-Smirnov Goodness-of-Fit Test	348	(2)
p-Values and ``Best Fits''	350	(1)
Selecting Input Models without Data	351	(2)
Multivariate and Time-Series Input Models	353	(5)
Covariance and Correlation	354	(1)
Multivariate Input Models	354	(2)
Time-Series Input Models	356	(2)
Summary	358	(9)
References	359	(1)
Exercises	360	(7)
Virification and Validation of Simulation Models	367	(31)
Model Building, Verification, and Validation	368	(1)
Verification of Simulation Models	369	(5)
Calibration and Validation of Models	374	(19)
Face Validity	376	(1)
Validation of Model Assumptions	377	(1)
Validating Input-Output Transformations	377	(11)
Input-Output Validation: Using Historical Input Data	388	(4)
Input-Output Validation: Using a Turing Test	392	(1)
Summary	393	(5)
References	394	(1)
Exercises	395	(3)
Output Analysis for a Single Model	398	(52)
Types of Simulations with Respect to Output Analysis	399	(3)
Stochastic Nature of Output Data	402	(5)
Measures of Performance and Their Estimation	407	(3)
Point Estimation	407	(2)
Interval Estimation	409	(1)
Output Analysis for Terminating Simulations	410	(8)
Statistical Background	410	(1)
Confidence-Interval Estimation for a Fixed Number of Replications	411	(3)
Confidence Intervals with Specified Precision	414	(2)
Confidence Intervals for Quantiles	416	(2)
Output Analysis for Steady-State Simulations	418	(23)
Initialization Bias in Steady-State Simulations	419	(7)
Statistical Background	426	(4)
Replication Method for Steady-State Simulations	430	(4)
Sample Size in Steady-State Simulations	434	(2)
Batch Means for Interval Estimation in Steady-State Simulations	436	(4)
Confidence Intervals for Quantiles	440	(1)
Summary	441	(9)
References	441	(1)
Exercises	442	(8)
Comparison and Evaluation of Alternative System Designs	450	(52)
Comparison of Two System Designs	451	(16)
Independent Sampling with Equal Variances	454	(2)
Independent Sampling with Unequal Variances	456	(1)
Correlated Sampling, or Common Random Numbers	456	(10)
Confidence Intervals with Specified Precision	466	(1)
Comparison of Several System Designs	467	(9)
Bonferroni Approach to Multiple Comparisons	468	(5)
Bonferroni Approach to Selecting the Best	473	(3)
Metamodeling	476	(9)
Simple Linear Regression	477	(4)
Testing for Significance of Regression	481	(3)
Multiple Linear Regression	484	(1)
Random-Number Assignment for Regression	484	(1)
Optimization via Simulation	485	(10)
What Does ``Optimization via Simulation'' Mean?	487	(1)
Why Is Optimization via Simulation Difficult?	488	(1)
Using Robust Heuristics	489	(3)
An Illustration: Random Search	492	(3)
Summary	495	(7)
References	496	(1)
Exercises	497	(5)
Simulation of Manufacturing and Material Handling Systems	502	(26)
Manufacturing and Material Handling Simulations	502	(5)
Models of Manufacturing Systems	503	(2)
Models of Material Handling	505	(1)
Some Common Material Handling Equipment	506	(1)
Goals and Performance Measures	507	(1)
Issues in Manufacturing and Material Handling Simulations	508	(7)
Modeling Downtimes and Failures	508	(5)
Trace-Driven Models	513	(2)
Case Studies of the Simulation of Manufacturing and Material Handling Systems	515	(2)
Summary	517	(11)
References	517	(1)
Exercises	518	(10)
Simulation of Computer Systems	528	(43)
Introduction	528	(3)
Simulation Tools	531	(11)
Process Orientation	533	(4)
Event Orientation	537	(5)
Model Input	542	(11)
Modulated Poisson Process	543	(4)
Virtual Memory Referencing	547	(6)
High-Level Computer-System Simulation	553	(4)
CPU Simulation	557	(6)
Memory Simulation	563	(3)
Summary	566	(5)
References	567	(1)
Exercises	568	(3)
Appendix Tables	571	(16)
A.1 Random Digits	572	(1)
A.2 Random Normal Numbers	573	(1)
A.3 Cumulative Normal Distribution	574	(2)
A.4 Cumulative Poisson Distribution	576	(4)
A.5 Percentage Points of the Students t Distribution with v Degrees of Freedom	580	(1)
A.6 Percentage Points of the Chi-Square Distribution with v Degrees of Freedom	581	(1)
A.7 Percentage Points of the F Distribution with α = 0.05	582	(1)
A.8 Kolmogorov-Smirnov Critical Values	583	(1)
A.9 Maximum-Likelihood Estimates of the Gamma Distribution	584	(1)
A.10 Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n	585	(1)
A.11 Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n	586	(1)
Index	587