Discrete-event system simulation / Jerry Banks, John S. Carson II, Barry L. Nelson and David M. Nicol

By: Contributor(s): Material type: TextTextPublication details: Upper Saddle River, New Jersey : Prentice-Hall, c2001Edition: Third editionDescription: xiv, 594 pages : illustrations ; 24 cmISBN:
  • 130887021
Subject(s): LOC classification:
  • QA 76.9.C65 .B36 2001
Contents:
1.1 When Simulation Is the Appropriate Tool 4 -- 1.2 When Simulation Is Not Appropriate 5 -- 1.3 Advantages and Disadvantages of Simulation 6 -- 1.4 Areas of Application 7 -- 1.5 Systems and System Environment 9 -- 1.6 Components of a System 10 -- 1.7 Discrete and Continuous Systems 12 -- 1.8 Model of a System 13 -- 1.9 Types of Models 13 -- 1.10 Discrete-Event System Simulation 14 -- 1.11 Steps in a Simulation Study 15 -- 2 Simulation Examples 23 -- 2.1 Simulation of Queueing Systems 24 -- 2.2 Simulation of Inventory Systems 41 -- 2.3 Other Examples of Simulation 47 -- 3.1 Concepts in Discrete-Event Simulation 64 -- 3.1.1 Event-Scheduling/Time-Advance Algorithm 67 -- 3.1.2 World Views 72 -- 3.1.3 Manual Simulation Using Event Scheduling 75 -- 3.2 List Processing 85 -- 3.2.1 Lists: Basic Properties and Operations 86 -- 3.2.2 Using Arrays for List Processing 87 -- 3.2.3 Using Dynamic Allocation and Linked Lists 90 -- 3.2.4 Advanced Techniques 92 -- 4 Simulation Software 95 -- 4.1 History of Simulation Software 96 -- 4.1.1 Period of Search (1955-60) 97 -- 4.1.2 Advent (1961-65) 97 -- 4.1.3 Formative Period (1966-70) 98 -- 4.1.4 Expansion Period (1971-78) 98 -- 4.1.5 Consolidation and Regeneration (1979-86) 99 -- 4.1.6 Present Period (1987-present) 99 -- 4.2 Selection of Simulation Software 100 -- 4.3 An Example Simulation 104 -- 4.4 Simulation in C++ 104 -- 4.5 Simulation in GPSS 114 -- 4.6 Simulation in CSIM 119 -- 4.7 Simulation Packages 123 -- 4.7.1 Arena 123 -- 4.7.2 AutoMod 124 -- 4.7.3 Deneb/QUEST 125 -- 4.7.4 Extend 126 -- 4.7.5 Micro Saint 127 -- 4.7.6 ProModel 127 -- 4.7.7 Taylor ED 128 -- 4.7.8 WITNESS 128 -- 4.8 Experimentation and Statistical Analysis Tools 129 -- 4.8.1 Common Features 129 -- 4.8.2 Analysis Tools 129 -- 4.9 Trends in Simulation Software 131 -- 4.9.1 High-Fidelity Simulation 131 -- 4.9.2 Data Exchange Standards 132 -- 4.9.3 Internet 132 -- 4.9.4 Old Paradigm versus New Paradigm 133 -- 4.9.5 Component Libraries 133 -- 4.9.6 Distributed Manufacturing Simulation/High Level Architecture 133 -- 4.9.7 Embedded Simulation 134 -- 4.9.8 Optimization 134 -- Part 2 Mathematical and Statistical Models -- 5 Statistical Models In Simulation 153 -- 5.2 Useful Statistical Models 160 -- 5.3 Discrete Distributions 165 -- 5.4 Continuous Distributions 170 -- 5.5 Poisson Process 190 -- 5.6 Empirical Distributions 193 -- 6 Queueing Models 204 -- 6.1 Characteristics of Queueing Systems 205 -- 6.1.1 Calling Population 206 -- 6.1.2 System Capacity 207 -- 6.1.3 Arrival Process 207 -- 6.1.4 Queue Behavior and Queue Discipline 209 -- 6.1.5 Service Times and the Service Mechanism 209 -- 6.2 Queueing Notation 211 -- 6.3 Long-Run Measures of Performance of Queueing Systems 212 -- 6.3.1 Time-Average Number in System L 213 -- 6.3.2 Average Time Spent in System per Customer, w 215 -- 6.3.3 Conservation Equation: L = [lambad]w 216 -- 6.3.4 Server Utilization 218 -- 6.3.5 Costs in Queueing Problems 223 -- 6.4 Steady-State Behavior of Infinite-Population Markovian Models 224 -- 6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 225 -- 6.4.2 Multiserver Queue: M/M/c/[infinity]/[infinity] 231 -- 6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/[infinity] 237 -- 6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 239 -- 6.6 Networks of Queues 243 -- Part 3 Random Numbers -- 7 Random-Number Generation 255 -- 7.1 Properties of Random Numbers 255 -- 7.2 Generation of Pseudo-Random Numbers 256 -- 7.3 Techniques for Generating Random Numbers 258 -- 7.3.1 Linear Congruential Method 258 -- 7.3.2 Combined Linear Congruential Generators 262 -- 7.4 Tests for Random Numbers 264 -- 7.4.1 Frequency Tests 266 -- 7.4.2 Runs Tests 270 -- 7.4.3 Tests for Autocorrelation 278 -- 7.4.4 Gap Test 281 -- 7.4.5 Poker Test 283 -- 8 Random-Variate Generation 289 -- 8.1 Inverse Transform Technique 290 -- 8.1.1 Exponential Distribution 290 -- 8.1.2 Uniform Distribution 294 -- 8.1.3 Weibull Distribution 294 -- 8.1.4 Triangular Distribution 295 -- 8.1.5 Empirical Continuous Distributions 296 -- 8.1.6 Continuous Distributions without a Closed-Form Inverse 300 -- 8.1.7 Discrete Distributions 301 -- 8.2 Direct Transformation for the Normal and Lognormal Distributions 307 -- 8.3 Convolution Method 309 -- 8.3.1 Erlang Distribution 309 -- 8.4 Acceptance-Rejection Technique 310 -- 8.4.1 Poisson Distribution 311 -- 8.4.2 Gamma Distribution 314 -- Part 4 Analysis of Simulation Data -- 9 Input Modeling 323 -- 9.1 Data Collection 324 -- 9.2 Identifying the Distribution with Data 327 -- 9.2.1 Histograms 327 -- 9.2.2 Selecting the Family of Distributions 331 -- 9.2.3 Quantile-Quantile Plots 333 -- 9.3 Parameter Estimation 336 -- 9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 336 -- 9.3.2 Suggested Estimators 338 -- 9.4 Goodness-of-Fit Tests 343 -- 9.4.1 Chi-Square Test 343 -- 9.4.2 Chi-Square Test with Equal Probabilities 346 -- 9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test 348 -- 9.4.4 p-Values and "Best Fits" 350 -- 9.5 Selecting Input Models without Data 351 -- 9.6 Multivariate and Time-Series Input Models 353 -- 9.6.1 Covariance and Correlation 354 -- 9.6.2 Multivariate Input Models 354 -- 9.6.3 Time-Series Input Models 356 -- 10 Verification and Validation of Simulation Models 367 -- 10.1 Model Building, Verification, and Validation 368 -- 10.2 Verification of Simulation Models 369 -- 10.3 Calibration and Validation of Models 374 -- 10.3.1 Face Validity 376 -- 10.3.2 Validation of Model Assumptions 377 -- 10.3.3 Validating Input-Output Transformations 377 -- 10.3.4 Input-Output Validation: Using Historical Input Data 388 -- 10.3.5 Input-Ouput Validation: Using a Turing Test 392 -- 11 Output Analysis for a Single Model 398 -- 11.1 Types of Simulations with Respect to Output Analysis 399 -- 11.2 Stochastic Nature of Output Data 402 -- 11.3 Measures of Performance and Their Estimation 407 -- 11.3.1 Point Estimation 407 -- 11.3.2 Interval Estimation 409 -- 11.4 Output Analysis for Terminating Simulations 410 -- 11.4.1 Statistical Background 410 -- 11.4.2 Confidence-Interval Estimation for a Fixed Number of Replications 411 -- 11.4.3 Confidence Intervals with Specified Precision 414 -- 11.4.4 Confidence Intervals for Quantiles 416 -- 11.5 Output Analysis for Steady-State Simulations 418 -- 11.5.1 Initialization Bias in Steady-State Simulations 419 -- 11.5.2 Statistical Background 426 -- 11.5.3 Replication Method for Steady-State Simulations 430 -- 11.5.4 Sample Size in Steady-State Simulations 434 -- 11.5.5 Batch Means for Interval Estimation in Steady-State Simulations 436 -- 11.5.6 Confidence Intervals for Quantiles 440 -- 12 Comparison and Evaluation of Alternative System Designs 450 -- 12.1 Comparison of Two System Designs 451 -- 12.1.1 Independent Sampling with Equal Variances 454 -- 12.1.2 Independent Sampling with Unequal Variances 456 -- 12.1.3 Correlated Sampling, or Common Random Numbers 456 -- 12.1.4 Confidence Intervals with Specified Precision 466 -- 12.2 Comparison of Several System Designs 467 -- 12.2.1 Bonferroni Approach to Multiple Comparisons 468 -- 12.2.2 Bonferroni Approach to Selecting the Best 473 -- 12.3 Metamodeling 476 -- 12.3.1 Simple Linear Regression 477 -- 12.3.2 Testing for Significance of Regression 481 -- 12.3.3 Multiple Linear Regression 484 -- 12.3.4 Random-Number Assignment for Regression 484 -- 12.4 Optimization via Simulation 485 -- 12.4.1 What Does "Optimization via Simulation" Mean? 487 -- 12.4.2 Why Is Optimization via Simulation Difficult? 488 -- 12.4.3 Using Robust Heuristics 489 -- 12.4.4 An Illustration: Random Search 492 -- 13 Simulation of Manufacturing and Material Handling Systems 502 -- 13.1 Manufacturing and Material Handling Simulations 502 -- 13.1.1 Models of Manufacturing Systems 503 -- 13.1.2 Models of Material Handling 505 -- 13.1.3 Some Common Material Handling Equipment 506 -- 13.2 Goals and Performance Measures 507 -- 13.3 Issues in Manufacturing and Material Handling Simulations 508 -- 13.3.1 Modeling Downtimes and Failures 508 -- 13.3.2 Trace-Driven Models 513 -- 13.4 Case Studies of the Simulation of Manufacturing and Material Handling Systems 515 -- 14 Simulation of Computer Systems 528 -- 14.2 Simulation Tools 531 -- 14.2.1 Process Orientation 533 -- 14.2.2 Event Orientation 537 -- 14.3 Model Input 542 -- 14.3.1 Modulated Poisson Process 543 -- 14.3.2 Virtual Memory Referencing 547 -- 14.4 High-Level Computer-System Simulation 553 -- 14.5
CPU Simulation 557 -- 14.6 Memory Simulation 563 -- A.1 Random Digits 572 -- A.2 Random Normal Numbers 573 -- A.3 Cumulative Normal Distribution 574 -- A.4 Cumulative Poisson Distribution 576 -- A.5 Percentage Points of the Students t Distribution with v Degrees of Freedom 580 -- A.6 Percentage Points of the Chi-Square Distribution with v Degrees of Freedom 581 -- A.7 Percentage Points of the F Distribution with [alpha] = 0.05 582 -- A.8 Kolmogorov-Smirnov Critical Values 583 -- A.9 Maximum-Likelihood Estimates of the Gamma Distribution 584 -- A.10 Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n 585 -- A.11 Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n 586.
Summary: This book provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. Includes simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software. Covers properties, modeling and random-variate generation from the lognormal distribution. Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. For readers wanting to learn more about system simulation.
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Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Books Books National University - Manila LRC - Main General Circulation Gen. Ed. - CCIT GC QA 76.9.C65 .B36 2001 (Browse shelf(Opens below)) c.1 Available NULIB000002225

Includes bibliographical references and index.

1.1 When Simulation Is the Appropriate Tool 4 -- 1.2 When Simulation Is Not Appropriate 5 -- 1.3 Advantages and Disadvantages of Simulation 6 -- 1.4 Areas of Application 7 -- 1.5 Systems and System Environment 9 -- 1.6 Components of a System 10 -- 1.7 Discrete and Continuous Systems 12 -- 1.8 Model of a System 13 -- 1.9 Types of Models 13 -- 1.10 Discrete-Event System Simulation 14 -- 1.11 Steps in a Simulation Study 15 -- 2 Simulation Examples 23 -- 2.1 Simulation of Queueing Systems 24 -- 2.2 Simulation of Inventory Systems 41 -- 2.3 Other Examples of Simulation 47 -- 3.1 Concepts in Discrete-Event Simulation 64 -- 3.1.1 Event-Scheduling/Time-Advance Algorithm 67 -- 3.1.2 World Views 72 -- 3.1.3 Manual Simulation Using Event Scheduling 75 -- 3.2 List Processing 85 -- 3.2.1 Lists: Basic Properties and Operations 86 -- 3.2.2 Using Arrays for List Processing 87 -- 3.2.3 Using Dynamic Allocation and Linked Lists 90 -- 3.2.4 Advanced Techniques 92 -- 4 Simulation Software 95 -- 4.1 History of Simulation Software 96 -- 4.1.1 Period of Search (1955-60) 97 -- 4.1.2 Advent (1961-65) 97 -- 4.1.3 Formative Period (1966-70) 98 -- 4.1.4 Expansion Period (1971-78) 98 -- 4.1.5 Consolidation and Regeneration (1979-86) 99 -- 4.1.6 Present Period (1987-present) 99 -- 4.2 Selection of Simulation Software 100 -- 4.3 An Example Simulation 104 -- 4.4 Simulation in C++ 104 -- 4.5 Simulation in GPSS 114 -- 4.6 Simulation in CSIM 119 -- 4.7 Simulation Packages 123 -- 4.7.1 Arena 123 -- 4.7.2 AutoMod 124 -- 4.7.3 Deneb/QUEST 125 -- 4.7.4 Extend 126 -- 4.7.5 Micro Saint 127 -- 4.7.6 ProModel 127 -- 4.7.7 Taylor ED 128 -- 4.7.8 WITNESS 128 -- 4.8 Experimentation and Statistical Analysis Tools 129 -- 4.8.1 Common Features 129 -- 4.8.2 Analysis Tools 129 -- 4.9 Trends in Simulation Software 131 -- 4.9.1 High-Fidelity Simulation 131 -- 4.9.2 Data Exchange Standards 132 -- 4.9.3 Internet 132 -- 4.9.4 Old Paradigm versus New Paradigm 133 -- 4.9.5 Component Libraries 133 -- 4.9.6 Distributed Manufacturing Simulation/High Level Architecture 133 -- 4.9.7 Embedded Simulation 134 -- 4.9.8 Optimization 134 -- Part 2 Mathematical and Statistical Models -- 5 Statistical Models In Simulation 153 -- 5.2 Useful Statistical Models 160 -- 5.3 Discrete Distributions 165 -- 5.4 Continuous Distributions 170 -- 5.5 Poisson Process 190 -- 5.6 Empirical Distributions 193 -- 6 Queueing Models 204 -- 6.1 Characteristics of Queueing Systems 205 -- 6.1.1 Calling Population 206 -- 6.1.2 System Capacity 207 -- 6.1.3 Arrival Process 207 -- 6.1.4 Queue Behavior and Queue Discipline 209 -- 6.1.5 Service Times and the Service Mechanism 209 -- 6.2 Queueing Notation 211 -- 6.3 Long-Run Measures of Performance of Queueing Systems 212 -- 6.3.1 Time-Average Number in System L 213 -- 6.3.2 Average Time Spent in System per Customer, w 215 -- 6.3.3 Conservation Equation: L = [lambad]w 216 -- 6.3.4 Server Utilization 218 -- 6.3.5 Costs in Queueing Problems 223 -- 6.4 Steady-State Behavior of Infinite-Population Markovian Models 224 -- 6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 225 -- 6.4.2 Multiserver Queue: M/M/c/[infinity]/[infinity] 231 -- 6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/[infinity] 237 -- 6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 239 -- 6.6 Networks of Queues 243 -- Part 3 Random Numbers -- 7 Random-Number Generation 255 -- 7.1 Properties of Random Numbers 255 -- 7.2 Generation of Pseudo-Random Numbers 256 -- 7.3 Techniques for Generating Random Numbers 258 -- 7.3.1 Linear Congruential Method 258 -- 7.3.2 Combined Linear Congruential Generators 262 -- 7.4 Tests for Random Numbers 264 -- 7.4.1 Frequency Tests 266 -- 7.4.2 Runs Tests 270 -- 7.4.3 Tests for Autocorrelation 278 -- 7.4.4 Gap Test 281 -- 7.4.5 Poker Test 283 -- 8 Random-Variate Generation 289 -- 8.1 Inverse Transform Technique 290 -- 8.1.1 Exponential Distribution 290 -- 8.1.2 Uniform Distribution 294 -- 8.1.3 Weibull Distribution 294 -- 8.1.4 Triangular Distribution 295 -- 8.1.5 Empirical Continuous Distributions 296 -- 8.1.6 Continuous Distributions without a Closed-Form Inverse 300 -- 8.1.7 Discrete Distributions 301 -- 8.2 Direct Transformation for the Normal and Lognormal Distributions 307 -- 8.3 Convolution Method 309 -- 8.3.1 Erlang Distribution 309 -- 8.4 Acceptance-Rejection Technique 310 -- 8.4.1 Poisson Distribution 311 -- 8.4.2 Gamma Distribution 314 -- Part 4 Analysis of Simulation Data -- 9 Input Modeling 323 -- 9.1 Data Collection 324 -- 9.2 Identifying the Distribution with Data 327 -- 9.2.1 Histograms 327 -- 9.2.2 Selecting the Family of Distributions 331 -- 9.2.3 Quantile-Quantile Plots 333 -- 9.3 Parameter Estimation 336 -- 9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 336 -- 9.3.2 Suggested Estimators 338 -- 9.4 Goodness-of-Fit Tests 343 -- 9.4.1 Chi-Square Test 343 -- 9.4.2 Chi-Square Test with Equal Probabilities 346 -- 9.4.3 Kolmogorov-Smirnov Goodness-of-Fit Test 348 -- 9.4.4 p-Values and "Best Fits" 350 -- 9.5 Selecting Input Models without Data 351 -- 9.6 Multivariate and Time-Series Input Models 353 -- 9.6.1 Covariance and Correlation 354 -- 9.6.2 Multivariate Input Models 354 -- 9.6.3 Time-Series Input Models 356 -- 10 Verification and Validation of Simulation Models 367 -- 10.1 Model Building, Verification, and Validation 368 -- 10.2 Verification of Simulation Models 369 -- 10.3 Calibration and Validation of Models 374 -- 10.3.1 Face Validity 376 -- 10.3.2 Validation of Model Assumptions 377 -- 10.3.3 Validating Input-Output Transformations 377 -- 10.3.4 Input-Output Validation: Using Historical Input Data 388 -- 10.3.5 Input-Ouput Validation: Using a Turing Test 392 -- 11 Output Analysis for a Single Model 398 -- 11.1 Types of Simulations with Respect to Output Analysis 399 -- 11.2 Stochastic Nature of Output Data 402 -- 11.3 Measures of Performance and Their Estimation 407 -- 11.3.1 Point Estimation 407 -- 11.3.2 Interval Estimation 409 -- 11.4 Output Analysis for Terminating Simulations 410 -- 11.4.1 Statistical Background 410 -- 11.4.2 Confidence-Interval Estimation for a Fixed Number of Replications 411 -- 11.4.3 Confidence Intervals with Specified Precision 414 -- 11.4.4 Confidence Intervals for Quantiles 416 -- 11.5 Output Analysis for Steady-State Simulations 418 -- 11.5.1 Initialization Bias in Steady-State Simulations 419 -- 11.5.2 Statistical Background 426 -- 11.5.3 Replication Method for Steady-State Simulations 430 -- 11.5.4 Sample Size in Steady-State Simulations 434 -- 11.5.5 Batch Means for Interval Estimation in Steady-State Simulations 436 -- 11.5.6 Confidence Intervals for Quantiles 440 -- 12 Comparison and Evaluation of Alternative System Designs 450 -- 12.1 Comparison of Two System Designs 451 -- 12.1.1 Independent Sampling with Equal Variances 454 -- 12.1.2 Independent Sampling with Unequal Variances 456 -- 12.1.3 Correlated Sampling, or Common Random Numbers 456 -- 12.1.4 Confidence Intervals with Specified Precision 466 -- 12.2 Comparison of Several System Designs 467 -- 12.2.1 Bonferroni Approach to Multiple Comparisons 468 -- 12.2.2 Bonferroni Approach to Selecting the Best 473 -- 12.3 Metamodeling 476 -- 12.3.1 Simple Linear Regression 477 -- 12.3.2 Testing for Significance of Regression 481 -- 12.3.3 Multiple Linear Regression 484 -- 12.3.4 Random-Number Assignment for Regression 484 -- 12.4 Optimization via Simulation 485 -- 12.4.1 What Does "Optimization via Simulation" Mean? 487 -- 12.4.2 Why Is Optimization via Simulation Difficult? 488 -- 12.4.3 Using Robust Heuristics 489 -- 12.4.4 An Illustration: Random Search 492 -- 13 Simulation of Manufacturing and Material Handling Systems 502 -- 13.1 Manufacturing and Material Handling Simulations 502 -- 13.1.1 Models of Manufacturing Systems 503 -- 13.1.2 Models of Material Handling 505 -- 13.1.3 Some Common Material Handling Equipment 506 -- 13.2 Goals and Performance Measures 507 -- 13.3 Issues in Manufacturing and Material Handling Simulations 508 -- 13.3.1 Modeling Downtimes and Failures 508 -- 13.3.2 Trace-Driven Models 513 -- 13.4 Case Studies of the Simulation of Manufacturing and Material Handling Systems 515 -- 14 Simulation of Computer Systems 528 -- 14.2 Simulation Tools 531 -- 14.2.1 Process Orientation 533 -- 14.2.2 Event Orientation 537 -- 14.3 Model Input 542 -- 14.3.1 Modulated Poisson Process 543 -- 14.3.2 Virtual Memory Referencing 547 -- 14.4 High-Level Computer-System Simulation 553 -- 14.5

CPU Simulation 557 -- 14.6 Memory Simulation 563 -- A.1 Random Digits 572 -- A.2 Random Normal Numbers 573 -- A.3 Cumulative Normal Distribution 574 -- A.4 Cumulative Poisson Distribution 576 -- A.5 Percentage Points of the Students t Distribution with v Degrees of Freedom 580 -- A.6 Percentage Points of the Chi-Square Distribution with v Degrees of Freedom 581 -- A.7 Percentage Points of the F Distribution with [alpha] = 0.05 582 -- A.8 Kolmogorov-Smirnov Critical Values 583 -- A.9 Maximum-Likelihood Estimates of the Gamma Distribution 584 -- A.10 Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n 585 -- A.11 Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n 586.

This book provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. Includes simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software. Covers properties, modeling and random-variate generation from the lognormal distribution. Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. For readers wanting to learn more about system simulation.

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