Discrete-Event System Simulation
Discrete-Event System Simulation
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Author(s): Banks, Jerry
Carson, John S., II
Carson, John S.
Carson, John, II
Nicol, David
ISBN No.: 9780136062127
Edition: Revised
Pages: 648
Year: 200908
Format: Trade Paper
Price: $ 319.34
Dispatch delay: Dispatched between 7 to 15 days
Status: Available

I Introduction to Discrete-Event System Simulation 1 Chapter 1 Introduction to Simulation 3 1.1 When Simulation Is the Appropriate Tool 4 1.2 When Simulation Is Not Appropriate 4 1.3 Advantages and Disadvantages of Simulation 5 1.4 Areas of Application 7 1.5 Systems and System Environment 9 1.6 Components of a System 9 1.7 Discrete and Continuous Systems 11 1.


8 Model of a System 12 1.9 Types of Models 13 1.10 Discrete-Event System Simulation 13 1.11 Steps in a Simulation Study 14 References 18 Exercises 19 Chapter 2 Simulation Examples 21 2.1 Simulation of Queueing Systems 22 2.2 Simulation of Inventory Systems 39 2.3 Other Examples of Simulation 46 2.4 Summary 57 References 57 Exercises 57 Chapter 3 General Principles 67 3.


1 Concepts in Discrete-Event Simulation 68 3.1.1 The Event Scheduling/Time Advance Algorithm 71 3.1.2 World Views 74 3.1.3 Manual Simulation Using Event Scheduling 77 3.2 List Processing 86 3.


2.1 Lists: Basic Properties and Operations 87 3.2.2 Using Arrays for List Processing 88 3.2.3 Using Dynamic Allocation and Linked Lists 90 3.2.4 Advanced Techniques 92 3.


3 Summary 92 References 92 Exercises 93 Chapter 4 Simulation Software 95 4.1 History of Simulation Software 96 4.1.1 The Period of Search (1955--60) 97 4.1.2 The Advent (1961--65) 97 4.1.3 The Formative Period (1966--70) 97 4.


1.4 The Expansion Period (1971--78) 98 4.1.5 Consolidation and Regeneration (1979--86) 98 4.1.6 Integrated Environments (1987--Present) 99 4.2 Selection of Simulation Software 99 4.3 An Example Simulation 102 4.


4 Simulation in Java 104 4.5 Simulation in GPSS 112 4.6 Simulation in SSF 117 4.7 Simulation Software 120 4.7.1 Arena 122 4.7.2 AutoMod 123 4.


7.3 Extend 124 4.7.4 Flexsim 124 4.7.5 Micro Saint 125 4.7.6 ProModel 125 4.


7.7 QUEST 126 4.7.8 SIMUL8 127 4.7.9 WITNESS 128 4.8 Experimentation and Statistical-Analysis Tools 128 4.8.


1 Common Features 128 4.8.2 Products 129 References 131 Exercises 132 II Mathematical and Statistical Models 147 Chapter 5 Statistical Models in Simulation 149 5.1 Review of Terminology and Concepts 150 5.2 Useful Statistical Models 156 5.3 Discrete Distributions 160 5.4 Continuous Distributions 166 5.5 Poisson Process 186 5.


5.1 Properties of a Poisson Process 188 5.5.2 Nonstationary Poisson Process 189 5.6 Empirical Distributions 190 5.7 Summary 193 References 193 Exercises 193 Chapter 6 Queueing Models 201 6.1 Characteristics of Queueing Systems 202 6.1.


1 The Calling Population 202 6.1.2 System Capacity 204 6.1.3 The Arrival Process 204 6.1.4 Queue Behavior and Queue Discipline 205 6.1.


5 Service Times and the Service Mechanism 206 6.2 Queueing Notation 208 6.3 Long-Run Measures of Performance of Queueing Systems 208 6.3.1 Time-Average Number in System L 209 6.3.2 Average Time Spent in System Per Customer w 211 6.3.


3 The Conservation Equation: L = w 212 6.3.4 Server Utilization 213 6.3.5 Costs in Queueing Problems 218 6.4 Steady-State Behavior of Infinite-Population Markovian Models 220 6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 221 6.


4.2 Multiserver Queue: M/M/c// 227 6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/ 233 6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 235 6.6 Networks of Queues 239 6.7 Summary 241 References 242 Exercises 243 III Random Numbers 249 Chapter 7 Random-Number Generation 251 7.1 Properties of Random Numbers 251 7.


2 Generation of Pseudo-Random Numbers 252 7.3 Techniques for Generating Random Numbers 253 7.3.1 Linear Congruential Method 254 7.3.2 Combined Linear Congruential Generators 257 7.3.3 Random-Number Streams 259 7.


4 Tests for Random Numbers 260 7.4.1 Frequency Tests 261 7.4.2 Tests for Autocorrelation 265 7.5 Summary 267 References 268 Exercises 269 Chapter 8 Random-Variate Generation 272 8.1 Inverse-Transform Technique 273 8.1.


1 Exponential Distribution 273 8.1.2 Uniform Distribution 276 8.1.3 Weibull Distribution 277 8.1.4 Triangular Distribution 278 8.1.


5 Empirical Continuous Distributions 279 8.1.6 Continuous Distributions without a Closed-Form Inverse 283 8.1.7 Discrete Distributions 284 8.2 Acceptance--Rejection Technique 289 8.2.1 Poisson Distribution 290 8.


2.2 Nonstationary Poisson Process 293 8.2.3 Gamma Distribution 294 8.3 Special Properties 296 8.3.1 Direct Transformation for the Normal and Lognormal Distributions 296 8.3.


2 Convolution Method 298 8.3.3 More Special Properties 299 8.4 Summary 299 References 299 Exercises 300 IV Analysis of Simulation Data 305 Chapter 9 Input Modeling 307 9.1 Data Collection 308 9.2 Identifying the Distribution with Data 310 9.2.1 Histograms 310 9.


2.2 Selecting the Family of Distributions 313 9.2.3 Quantile--Quantile Plots 316 9.3 Parameter Estimation 319 9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 319 9.3.


2 Suggested Estimators 321 9.4 Goodness-of-Fit Tests 326 9.4.1 Chi-Square Test 327 9.4.2 Chi-Square Test with Equal Probabilities 329 9.4.3 Kolmogorov--Smirnov Goodness-of-Fit Test 331 9.


4.4 p -Values and "Best Fits" 333 9.5 Fitting a Nonstationary Poisson Process 334 9.6 Selecting Input Models without Data 335 9.7 Multivariate and Time-Series Input Models 337 9.7.1 Covariance and Correlation 337 9.7.


2 Multivariate Input Models 338 9.7.3 Time-Series Input Models 340 9.7.4 The Normal-to-Anything Transformation 342 9.8 Summary 344 References 345.


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