Simulation Modeling and Analysis with ARENA
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Table of contents
- Contentsv
- Prefacexvii
- Acknowledgmentsxxi
- Chapter 1: Introduction to Simulation Modeling1
- 1.1 Systems and Models1
- 1.2 Analytical Versus Simulation Modeling2
- 1.3 Simulation Modeling and Analysis4
- 1.4 Simulation Worldviews4
- 1.5 Model Building5
- 1.6 Simulation Costs and Risks6
- 1.7 Example: A Production Control Problem7
- 1.8 Project Report8
- Exercises10
- Chapter 2: Discrete Event Simulation11
- 2.1 Elements of Discrete Event Simulation11
- 2.2 Examples of DES Models13
- 2.2.1 Single Machine13
- 2.2.2 Single Machine with Failures13
- 2.2.3 Single Machine with an Inspection Station and Associated Inventory14
- 2.3 Monte Carlo Sampling and Histories15
- 2.3.1 Example: Work Station Subject to Failures and Inventory Control16
- 2.4 DES Languages19
- Exercises20
- Chapter 3: Elements of Probability and Statistics23
- 3.1 Elementary Probability Theory24
- 3.1.1 Probability Spaces25
- 3.1.2 Conditional Probabilities25
- 3.1.3 Dependence and Independence26
- 3.2 Random Variables27
- 3.3 Distribution Functions27
- 3.3.1 Probability Mass Functions28
- 3.3.2 Cumulative Distribution Functions28
- 3.3.3 Probability Density Functions28
- 3.3.4 Joint Distributions29
- 3.4 Expectations30
- 3.5 Moments30
- 3.6 Correlations32
- 3.7 Common Discrete Distributions33
- 3.7.1 Generic Discrete Distribution33
- 3.7.2 Bernoulli Distribution34
- 3.7.3 Binomial Distribution34
- 3.7.4 Geometric Distribution35
- 3.7.5 Poisson Distribution35
- 3.8 Common Continuous Distributions36
- 3.8.1 Uniform Distribution36
- 3.8.2 Step Distribution37
- 3.8.3 Triangular Distribution38
- 3.8.4 Exponential Distribution39
- 3.8.5 Normal Distribution40
- 3.8.6 Lognormal Distribution41
- 3.8.7 Gamma Distribution42
- 3.8.8 Student's t Distribution44
- 3.8.9 F Distribution45
- 3.8.10 Beta Distribution46
- 3.8.11 Weibull Distribution47
- 3.9 Stochastic Processes47
- 3.9.1 Iid Processes48
- 3.9.2 Poisson Processes48
- 3.9.3 Regenerative (Renewal) Processes49
- 3.9.4 Markov Processes49
- 3.10 Estimation50
- 3.11 Hypothesis Testing51
- Exercises52
- Chapter 4: Random Number and Variate Generation55
- 4.1 Variate and Process Generation56
- 4.2 Variate Generation Using the Inverse Transform Method57
- 4.2.1 Generation of Uniform Variates58
- 4.2.2 Generation of Exponential Variates58
- 4.2.3 Generation of Discrete Variates59
- 4.2.4 Generation of Step Variates from Histograms60
- 4.3 Process Generation61
- 4.3.1 Iid Process Generation61
- 4.3.2 Non-Iid Process Generation61
- Exercises63
- Chapter 5: Arena Basics65
- 5.1 Arena Home Screen66
- 5.1.1 Menu Bar67
- 5.1.2 Project Bar67
- 5.1.3 Standard Toolbar68
- 5.1.4 Draw and View Bars68
- 5.1.5 Animate and Animate Transfer Bars68
- 5.1.6 Run Interaction Bar69
- 5.1.7 Integration Bar69
- 5.1.8 Debug Bar69
- 5.2 Example: A Simple Workstation69
- 5.3 Arena Data Storage Objects74
- 5.3.1 Variables75
- 5.3.2 Expressions75
- 5.3.3 Attributes75
- 5.4 Arena Output Statistics Collection75
- 5.4.1 Statistics Collection via the Statistic Module76
- 5.4.2 Statistics Collection via the Record Module76
- 5.5 Arena Simulation and Output Reports77
- 5.6 Example: Two Processes in Series78
- 5.7 Example: A Hospital Emergency Room84
- 5.7.1 Problem Statement84
- 5.7.2 Arena Model85
- 5.7.3 Emergency Room Segment86
- 5.7.4 On-Call Doctor Segment93
- 5.7.5 Statistics Collection96
- 5.7.6 Simulation Output97
- 5.8 Specifying Time-Dependent Parameters via a Schedule100
- Exercises103
- Chapter 6: Model Testing and Debugging Facilities107
- 6.1 Facilities for Model Construction107
- 6.2 Facilities for Model Checking110
- 6.3 Facilities for Model Run Control111
- 6.3.1 Run Modes111
- 6.3.2 Mouse-Based Run Control111
- 6.3.3 Keyboard-Based Run Control112
- 6.4 Examples of Run Tracing114
- 6.4.1 Example: Open-Ended Tracing114
- 6.4.2 Example: Tracing Selected Blocks116
- 6.4.3 Example: Tracing Selected Entities117
- 6.5 Visualization and Animation118
- 6.5.1 Animate Connectors Button118
- 6.5.2 Animate Toolbar118
- 6.5.3 Animate Transfer Toolbar119
- 6.6 Arena Help Facilities119
- 6.6.1 Help Menu120
- 6.6.2 Help Button120
- Exercises120
- Chapter 7: Input Analysis123
- 7.1 Data Collection124
- 7.2 Data Analysis125
- 7.3 Modeling Time Series Data127
- 7.3.1 Method of Moments128
- 7.3.2 Maximal Likelihood Estimation Method129
- 7.4 Arena Input Analyzer130
- 7.5 Goodness-of-Fit Tests for Distributions134
- 7.5.1 Chi-Square Test134
- 7.5.2 Kolmogorov-Smirnov (K-S) Test137
- 7.6 Multimodal Distributions137
- Exercises138
- Chapter 8: Model Goodness: Verification and Validation141
- 8.1 Model Verification via Inspection of Test Runs142
- 8.1.1 Input Parameters and Output Statistics142
- 8.1.2 Using a Debugger143
- 8.1.3 Using Animation143
- 8.1.4 Sanity Checks143
- 8.2 Model Verification via Performance Analysis143
- 8.2.1 Generic Workstation as a Queueing System143
- 8.2.2 Queueing Processes and Parameters144
- 8.2.3 Service Disciplines145
- 8.2.4 Queueing Performance Measures145
- 8.2.5 Regenerative Queueing Systems and Busy Cycles146
- 8.2.6 Throughput147
- 8.2.7 Little's Formula148
- 8.2.8 Steady-State Flow Conservation148
- 8.2.9 PASTA Property149
- 8.3 Examples of Model Verification149
- 8.3.1 Model Verification in a Single Workstation149
- 8.3.2 Model Verification in Tandem Workstations153
- 8.4 Model Validation161
- Exercises162
- Chapter 9: Output Analysis165
- 9.1 Terminating and Steady-State Simulation Models166
- 9.1.1 Terminating Simulation Models166
- 9.1.2 Steady-State Simulation Models166
- 9.2 Statistics Collection from Replications168
- 9.2.1 Statistics Collection Using Independent Replications169
- 9.2.2 Statistics Collection Using Regeneration Points and Batch Means170
- 9.3 Point Estimation171
- 9.3.1 Point Estimation from Replications171
- 9.3.2 Point Estimation in Arena172
- 9.4 Confidence Interval Estimation173
- 9.4.1 Confidence Intervals for Terminating Simulations173
- 9.4.2 Confidence Intervals for Steady-State Simulations176
- 9.4.3 Confidence Interval Estimation in Arena176
- 9.5 Output Analysis via Standard Arena Output177
- 9.5.1 Working Example: A Workstation with Two Types of Parts177
- 9.5.2 Observation Collection179
- 9.5.3 Output Summary180
- 9.5.4 Statistics Summary: Multiple Replications181
- 9.6 Output Analysis via the Arena Output Analyzer182
- 9.6.1 Data Collection183
- 9.6.2 Graphical Statistics184
- 9.6.3 Batching Data for Independent Observations185
- 9.6.4 Confidence Intervals for Means and Variances186
- 9.6.5 Comparing Means and Variances187
- 9.6.6 Point Estimates for Correlations189
- 9.7 Parametric Analysis via the Arena Process Analyzer190
- Exercises193
- Chapter 10: Correlation Analysis195
- 10.1 Correlation in Input Analysis195
- 10.2 Correlation in Output Analysis197
- 10.3 Autocorrelation Modeling with TES Processes199
- 10.4 Introduction to TES Modeling200
- 10.4.1 Background TES Processes202
- 10.4.2 Foreground TES Processes205
- 10.4.3 Inversion of Distribution Functions211
- 10.5 Generation of TES Sequences215
- Generation of TES+ Sequences215
- Generation of TES- Sequences216
- Combining TES Generation Algorithms216
- 10.6 Example: Correlation Analysis in Manufacturing Systems219
- Exercises220
- Chapter 11: Modeling Production Lines223
- 11.1 Production Lines223
- 11.2 Models of Production Lines225
- 11.3 Example: A Packaging Line225
- 11.3.1 An Arena Model226
- 11.3.2 Manufacturing Process Modules226
- 11.3.3 Model Blocking Using the Hold Module227
- 11.3.4 Resources and Queues229
- 11.3.5 Statistics Collection230
- 11.3.6 Simulation Output Reports231
- 11.4 Understanding System Behavior and Model Verification237
- 11.5 Modeling Production Lines via Indexed Queues and Resources239
- 11.6 An Alternative Method of Modeling Blocking246
- 11.7 Modeling Machine Failures247
- 11.8 Estimating Distributions of Sojourn Times251
- 11.9 Batch Processing253
- 11.10 Assembly Operations256
- 11.11 Model Verification for Production Lines258
- Exercises259
- Chapter 12: Modeling Supply Chain Systems263
- 12.1 Example: A Production/Inventory System265
- 12.1.1 Problem Statement265
- 12.1.2 Arena Model266
- 12.1.3 Inventory Management Segment267
- 12.1.4 Demand Management Segment270
- 12.1.5 Statistics Collection272
- 12.1.6 Simulation Output273
- 12.1.7 Experimentation and Analysis274
- 12.2 Example: A Multiproduct Production/Inventory System276
- 12.2.1 Problem Statement276
- 12.2.2 Arena Model278
- 12.2.3 Inventory Management Segment278
- 12.2.4 Demand Management Segment284
- 12.2.5 Model Input Parameters and Statistics290
- 12.2.6 Simulation Results292
- 12.3 Example: A Multiechelon Supply Chain293
- 12.3.1 Problem Statement293
- 12.3.2 Arena Model295
- 12.3.3 Inventory Management Segment for Retailer295
- 12.3.4 Inventory Management Segment for Distribution Center297
- 12.3.5 Inventory Management Segment for Output Buffer299
- 12.3.6 Production/Inventory Management Segment for Input Buffer303
- 12.3.7 Inventory Management Segment for Supplier305
- 12.3.8 Statistics Collection305
- 12.3.9 Simulation Results306
- Exercises306
- Chapter 13: Modeling Transportation Systems313
- 13.1 Advanced Transfer Template Panel314
- 13.2 Animate Transfer Toolbar315
- 13.3 Example: A Bulk-Material Port316
- 13.3.1 Ship Arrivals317
- 13.3.2 Tug Boat Operations320
- 13.3.3 Coal-Loading Operations324
- 13.3.4 Tidal Window Modulation328
- 13.3.5 Simulation Results330
- 13.4 Example: A Toll Plaza332
- 13.4.1 Arrivals Generation334
- 13.4.2 Dispatching Cars to Tollbooths336
- 13.4.3 Serving Cars at Tollbooths340
- 13.4.4 Simulation Results for the Toll Plaza Model344
- 13.5 Example: A Gear Manufacturing Job Shop346
- 13.5.1 Gear Job Arrivals349
- 13.5.2 Gear Transportation351
- 13.5.3 Gear Processing353
- 13.5.4 Simulation Results for the Gear Manufacturing Job Shop Model358
- 13.6 Example: Sets Version of the Gear Manufacturing Job Shop Model359
- Exercises365
- Chapter 14: Modeling Computer Information Systems369
- 14.1 Client/Server System Architectures371
- 14.1.1 Message-Based Communications372
- 14.1.2 Client Hosts372
- 14.1.3 Server Hosts373
- 14.2 Communications Networks374
- 14.3 Two-Tier Client/Server Example: A Human Resources System375
- 14.3.1 Client Nodes Segment378
- 14.3.2 Communications Network Segment378
- 14.3.3 Server Node Segment380
- 14.3.4 Simulation Results383
- 14.4 Three-Tier Client/Server Example: An Online Bookseller System384
- 14.4.1 Request Arrivals and Transmission Network Segment386
- 14.4.2 Transmission Network Segment388
- 14.4.3 Server Nodes Segment391
- 14.4.4 Simulation Results399
- Exercises400
- Appendix A: Frequently Used Arena Constructs405
- A.1 Frequently Used Arena Built-in Variables405
- A.1.1 Entity-Related Attributes and Variables405
- A.1.2 Simulation Time Variables406
- A.1.3 Expressions406
- A.1.4 General-Purpose Global Variables406
- A.1.5 Queue Variables406
- A.1.6 Resource Variables406
- A.1.7 Statistics Collection Variables406
- A.1.8 Transporter Variables407
- A.1.9 Miscellaneous Variables and Functions407
- A.2 Frequently Used Arena Modules407
- A.2.1 Access Module (Advanced Transfer)407
- A.2.2 Assign Module (Basic Process)408
- A.2.3 Batch Module (Basic Process)408
- A.2.4 Create Module (Basic Process)408
- A.2.5 Decide Module (Basic Process)408
- A.2.6 Delay Module (Advanced Process)408
- A.2.7 Dispose Module (Basic Process)409
- A.2.8 Dropoff Module (Advanced Process)409
- A.2.9 Free Module (Advanced Transfer)409
- A.2.10 Halt Module (Advanced Transfer)409
- A.2.11 Hold Module (Advanced Process)410
- A.2.12 Match Module (Advanced Process)410
- A.2.13 PickStation Module (Advanced Transfer)410
- A.2.14 Pickup Module (Advanced Process)410
- A.2.15 Process Module (Basic Process)410
- A.2.16 ReadWrite Module (Advanced Process)411
- A.2.17 Record Module (Basic Process)411
- A.2.18 Release Module (Advanced Process)411
- A.2.19 Remove Module (Advanced Process)411
- A.2.20 Request Module (Advanced Transfer)411
- A.2.21 Route Module (Advanced Transfer)412
- A.2.22 Search Module (Advanced Process)412
- A.2.23 Seize Module (Advanced Process)412
- A.2.24 Separate Module (Basic Process)412
- A.2.25 Signal Module (Advanced Process)413
- A.2.26 Station Module (Advanced Transfer)413
- A.2.27 Store Module (Advanced Process)413
- A.2.28 Transport Module (Advanced Transfer)413
- A.2.29 Unstore Module (Advanced Process)413
- A.2.30 VBA Block (Blocks)414
- Appendix B: VBA in Arena415
- B.1 Arena’s Object Model416
- B.2 Arena’s Type Library416
- B.2.1 Resolving Object Name Ambiguities417
- B.2.2 Obtaining Access to the Application Object417
- B.3 Arena VBA Events417
- B.4 Example: Using VBA in Arena419
- B.4.1 Changing Inventory Parameters Just Before a Simulation Run419
- B.4.2 Changing Inventory Parameters during a Simulation Run421
- B.4.3 Changing Customer Arrival Distributions Just before a Simulation Run422
- B.4.3 Writing Arena Data to Excel via VBA Code424
- B.4.4 Reading Arena Data from Excel via VBA Code428
- References431
- Index435
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment. It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings.
· Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems
· Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems
· Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling
* Ample end-of-chapter problems and full Solutions Manual
* Includes CD with sample ARENA modeling programs
· Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems
· Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems
· Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling
* Ample end-of-chapter problems and full Solutions Manual
* Includes CD with sample ARENA modeling programs