Applied simulation modeling:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Belmont, Calif.
Thompson
2003
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXVIII, 456 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 0534381596 |
Internformat
MARC
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| 245 | 1 | 0 | |a Applied simulation modeling |c Andrew F. Seila ; Vlatko Ceric ; Pandu Tadikamalla |
| 264 | 1 | |a Belmont, Calif. |b Thompson |c 2003 | |
| 300 | |a XXXVIII, 456 S. |b Ill., graph. Darst. |e 1 CD-ROM (12 cm) | ||
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Datensatz im Suchindex
| _version_ | 1804130522387972096 |
|---|---|
| adam_text | Contents
Preface xix
List of Figures xxiv
List of Tables xxxiii
List of Models xxxv
About the Authors xxxvii
1 Introduction to Simulation 1
1.1 Decisions and Decision Models 2
1.2 Getting Answers from Models 5
1.3 A General Definition of Simulation 7
1.3.1 Gaming Simulations 9
vii
vni Contents
1.3.2 Static Simulations 9
1.3.3 Dynamic Systems Simulations 10
1.4 Components of a Simulation Study 11
1.5 Reasons for Using Simulation as an Analysis Tool 14
1.6 Summary 15
References 17
Problems 18
2 Static Simulations 21
2.1 Introduction 22
2.2 A Model for Profit on a Special Sale Promotion 23
2.2.1 The Spreadsheet Model 23
2.2.2 Setting Up the Simulation 25
2.2.3 Running the Replications 27
2.2.4 Analyze the Output Data 29
2.2.5 Comments about Spreadsheet Simulations 30
2.3 Simulation for Sensitivity Analysis 31
2.3.1 A Financial Model for an Office Building 31
2.3.2 Sensitivity Analysis for Office Building Model 32
2.3.3 What If Calculations 33
2.3.4 Simulation for Sensitivity Analysis 35
2.4 Simulation: Sampling on the Computer 38
2.4.1 A Model to Estimate it 38
2.4.2 An Experiment to Estimate it 39
2.4.3 Analysis of the Data 40
2.5 Some Techniques for Generating Random Variates 41
2.5.1 Bernoulli Random Variates 42
2.5.2 Uniform Random Variates 43
2.5.3 Triangular Random Variates 43
2.5.4 Normal Random Variates 44
2.5.5 Exponential Random Variates 44
Contents b
2.5.6 Discrete Integer Valued Random Variates 45
2.5.7 Other Discrete Random Variates 45
2.5.8 The Inverse Transform Method 47
2.5.9 Special Considerations 48
2.6 Evaluating Decisions: A One Period Inventory Model 48
2.6.1 The Inventory Model Revisited 48
2.6.2 Optimal Order Quantity Using Simulation 49
2.6.3 Other Cost Functions 50
2.6.4 Sensitivity Analysis 52
2.7 Data Analysis for Static Simulations 55
2.7.7 Performing Independent Replications 55
2.7.2 Estimating the Mean from Independent Replications 57
2.8 Simulation with Spreadsheets: A More Complex Model 58
2.8.1 A More Realistic Real Estate Model 58
2.8.2 Risk Analysis for the Real Estate Model 60
2.8.3 Fitting Distributions for Uncertain Variables 60
2.8.4 Simulation Results 63
2.8.5 Alternative Decisions to Improve Performance 64
2.9 An Insurance Model 66
2.9.1 A Model for Loss Ratio for an Insurance Agency 67
2.9.2 The Simulation 69
2.9.3 Simulation Results 70
2.10 Summary 71
References 73
Problems 73
3 Financial Models and ©RISK 77
3.1 Introduction 78
3.2 A Model for the Price of a Stock 79
3.3 Options, Futures, and Simulation 88
3.3.1 Options and Futures 88
x Contents
3.3.2 Estimating the Price of a Call Option Using Simulation 89
3.3.3 Hedging Using Put Options 91
3.4 Dynamic Financial Models of Stock Prices 93
3.4.1 A Worksheet to Sample Stock Prices over Time 93
3.4.2 Estimating the Price of an Asian Option 96
3.5 Correlated Asset Values 99
3.5.1 Estimating the Correlation Matrix 99
3.5.2 Sampling Correlated Variates 101
3.6 Fitting a Distribution to Data 104
3.6.1 Using BestFit to Fit a Specific Distribution to the Data 106
3.6.2 Finding the Distribution with the Best Fit 110
3.7 Summary 113
References 114
Problems 115
4 Dynamic Simulations 117
4.1 Introduction 118
4.2 Waiting Times in a Single Server Queueing System 119
4.2.1 Lindley s Formula 119
4.2.2 Taxonomy of Queueing Systems 120
4.2.3 A Spreadsheet Simulation ofM/M/1 Queue Waiting Times 121
4.3 Characteristics of Data from Dynamic Simulations 122
4.3.1 The Initial Transient Period 123
4.3.2 Autocorrelated Observations 126
4.4 Batch Means to Estimate the Mean from Stationary Data 126
4.4.1 Batch Means Computation 127
4.4.2 Some Guidelines to Applying the Batch Means Method 128
4.5 Simulating Discrete Time Markov Chains 129
4.5.1 A Markov Chain Inventory Model 129
4.5.2 A Markov Chain Queueing Model 131
4.5.3 A Markov Chain Reliability Model 133
Contents
4.5.4 Performance Measures for Markov Chains 135
4.6 The Regenerative Method for Estimating the Mean 136
4.6.1 Regenerative Processes 137
4.6.2 The Regenerative Method for Estimating the Mean 138
4.7 An Advanced Queueing Model 142
4.8 A Marketing Model 143
4.9 Summary 149
References 150
Problems 151
5 System Modeling Concepts for 154
=—— Discrete Event Simulation
5.1 Introduction 155
5.1.1 Static and Dynamic Model Descriptions 156
5.2 Events and Event Sequencing 156
5.2.1 Events and State Changes 157
5.2.2 Event Scheduling 158
5.2.3 Discrete Event Simulation Model Development 159
5.2.4 A Generic Simulation Language 160
5.3 Example: A Single Server Queue 161
5.3.1 State Variables for the Single Server Queue 162
5.3.2 Event Routines for the Single Server Queue 162
5.3.3 A Detailed Description of Event Execution and State Changes 163
5.3.4 Review: Event Scheduling 167
5.3.5 Estimating the Mean Number in the Queue 169
5.4 Event Graphs 175
5.4.1 Simplifying an Event Graph 176
5.4.2 Conditional Events 178
5.4.3 Event Routines and State Changes 178
5.5 Event Parameters 178
xii Contents
5.5.1 Passing Event Parameters to Event Routines 179
5.5.2 Using Event Parameters in Event Routines 180
5.5.3 Event Parameters in Event Graphs 181
5.5.4 Event Parameters and Output Data Analysis 181
5.5.5 Event Canceling 187
5.6 Static Model Description 189
5.6.1 Entities and Attributes 189
5.6.2 Sets, Lists, and Queues 190
5.7 A Message Processing Model that Uses Entities and Lists 191
5.7.1 Top Down Design forModel Building 192
5.7.2 Top Level Model Description 193
5.7.3 Model Details 194
5.7.4 Model Output 195
5.8 A Single Server Queue Using Entities and Events 198
5.9 Summary 204
References 205
Problems 206
6 System Modeling Paradigms 209
6.1 Introduction 210
6.2 Review of Discrete Event Dynamic Systems Concepts 211
6.2.1 Temporary and Permanent Entities 211
6.2.2 Attributes and Entity States 212
6.2.3 System Dynamics 213
6.2.4 Conditional and Scheduled Events 214
6.2.5 Activities and Processes 215
6.3 The Activity View 216
6.3.1 Original Version 217
6.3.2 The Three Phase Approach 220
6.3.3 An Example of Simulation Execution 222
Using the Three Phase Approach
6.4 The Process View 225
Contents xi
6.4.1 The Process View of the Single Server Queue 226
6.4.2 Process Interaction 228
6.4.3 Processes, Events, and Activities 230
6.5 A Manufacturing Example 231
6.5.1 The Event View Model 233
6.5.2 The Activity View 236
6.5.3 The Process View 236
6.6 Summary 241
References 242
Problems 243
7 Visual Interactive Simulation and Arena 245
7.1 Visual Interactive Simulation 246
7.1.1 Simulation Hardware and Graphical User Interfaces 247
7.1.2 Software for VIS 248
7.2 A First Look at Arena 249
7.2.7 Build the Model 250
7.2.2 Preparing a Simulation Run 257
7.2.3 Interactive Model Runs 259
7.2.4 Reports 260
7.2.5 Modeling Costs in Arena 262
7.2.6 Modeling Elements 262
7.2.7 Modeling Blocks 263
7.3 A Bank Lobby Model in Arena 264
7.3.1 Bank Lobby System 264
7.3.2 Model Building 265
7.3.3 Simulation Run and Results 271
7.4 Visual Interactive Simulation: A Recap 274
7.5 Summary 276
References 277
Problems 277
xiv Contents
8 Graphical Simulation Modeling 279
8.1 Graphical Modeling 280
8.2 Some Graphical Modeling Techniques 282
8.2.1 Event Graphs 282
8.2.2 Activity Cycle Diagrams 283
8.2.3 PetriNets 285
8.3 Graphical Modeling with Arena 288
8.3.1 The Batch and Separate Modules 290
8.3.2 A Copy Center Model 291
8.3.3 The Arena Model 291
8.3.4 Running the Simulation 299
8.4 Hierarchical Modeling 303
8.4.1 Basic Concepts 303
8.4.2 Hierarchical Modeling with Arena 304
8.4.3 A Fuel Depot Model 304
8.5 Animation 309
8.5.1 Animation with Arena 310
8.5.2 The Fuel Depot Model and Facility Based Animation 310
8.5.3 Station Sequences 317
8.5.4 Named Views and Navigation 320
8.6 Summary 321
References 323
Problems 323
9 Problem Solving Using Simulation 326
9.1 Introduction 326
9.2 Service Systems 328
9.2.1 Characteristics of Service Systems 328
9.2.2 Resources 331
9.2.3 Multiple Servers and Multiple Queues 332
Contents :
9.2.4 Networks of Queues 337
9.2.5 Performance Measures for Service Systems 339
9.2.6 Cost Parameters 341
9.3 Manufacturing System Models 345
9.3.1 Characteristics of Manufacturing Systems 346
9.3.2 A Manufacturing System Model 347
9.3.3 Conveyor Modeling 348
9.3.4 Building the Model in Steps: Version 1 350
9.3.5 Modeling Blocking 353
9.3.6 Adding the Rework Loop: Version 2 355
9.3.7 Adding the Loop Conveyor 357
9.3.8 Special Characteristics: Transporters and Batch Processing 360
9.3.9 A Manufacturing Decision Support System 363
9.4 Transportation Systems 365
9.4.1 Phase 1: The Truck Unloading Process 369
9.4.2 Phase 2: Refining the Model 374
9.4.3 Phase 3: Data Collection and Experimental Design 378
9.5 Summary 382
References 384
Problems 384
10 Simulation in Practice 390
10.1 A Simulation Project 391
10.1.1 Project Origination and Systems Analysis 392
10.1.2 Data Collection 392
10.1.3 The Preliminary Model 394
10.1.4 Project Conclusion 395
10.2 The Science of Simulation Project Management: 396
Problem Analysis and Solution Process
10.2.1 Problem Formulation and Statement of Objectives 397
10.2.2 Project Plan and Schedule 397
10.2.3 System Analysis 398
10.2.4 Model Conceptualization and Formulation 398
xvi Contents
10.2.5 Data Collection and Analysis 399
10.2.6 Model Building 400
10.2.7 Model Validation 400
10.2.8 Experimentation and Analysis 401
10.2.9 Reports and Presentations 401
10.2.10 Implementation 402
10.3 The Art of Project Management 402
10.3.1 Problem Identification and Formulation 403
10.3.2 Understand the Client s Objectives 403
10.3.3 Collect Data to Verify and Document Problems 404
10.3.4 Consider Alternative Models 405
10.3.5 Project Planning and Management 405
10.3.6 Team Management 407
10.3.7 Communicating with Clients 408
10.4 Data Collection 409
10.4.1 Coordinating Model Development and Data Collection 409
10.4.2 The Amount and Availability of Data 410
10.4.3 Data Quality 410
10.5 Model Management 411
10.5.1 Model Complexity 411
10.5.2 Hierarchical Model Building 412
10.5.3 Start with a Simple Model 413
10.6 Summary 415
References 417
Appendix: Review of Probability and Statistics 418
A.1 Probability Concepts: Some Definitions 418
A.I.I Probability Rules 419
A.2 Random Variables, Probability Distributions, and Expectations 421
A.2.1 Discrete Random Variables 421
A.2.2 Some Discrete Distributions 423
A. 3 Continuous Random Variables 426
Contents xv
A.3.1 Some Continuous Distributions 427
A.4 Inferential Analysis 434
A.4.1 Sampling Distribution of a Statistic 435
A.4.2 Examples: Sampling Distribution ofX 437
A.4.3 Estimation 439
A.4.4 Confidence Intervals 440
A.4.5 Sample Size Determination 442
A.4.6 Hypothesis Testing 443
A.4.7 Examples: Hypothesis Testing 445
A.4.8 Testing on the Difference of Two (Population) Means 447
Index 449
|
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| id | DE-604.BV017835705 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T19:22:22Z |
| institution | BVB |
| isbn | 0534381596 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-010705725 |
| oclc_num | 248783199 |
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| owner | DE-M347 |
| owner_facet | DE-M347 |
| physical | XXXVIII, 456 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Thompson |
| record_format | marc |
| spelling | Seila, Andrew F. Verfasser aut Applied simulation modeling Andrew F. Seila ; Vlatko Ceric ; Pandu Tadikamalla Belmont, Calif. Thompson 2003 XXXVIII, 456 S. Ill., graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Simulation (DE-588)4055072-2 gnd rswk-swf Computersimulation (DE-588)4148259-1 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Computersimulation (DE-588)4148259-1 s DE-604 Simulation (DE-588)4055072-2 s Modellierung (DE-588)4170297-9 s Anwendung (DE-588)4196864-5 s b DE-604 Tadikamalla, Pandu Verfasser aut Ceric, Vlatko Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010705725&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Seila, Andrew F. Tadikamalla, Pandu Ceric, Vlatko Applied simulation modeling Simulation (DE-588)4055072-2 gnd Computersimulation (DE-588)4148259-1 gnd Anwendung (DE-588)4196864-5 gnd Modellierung (DE-588)4170297-9 gnd |
| subject_GND | (DE-588)4055072-2 (DE-588)4148259-1 (DE-588)4196864-5 (DE-588)4170297-9 |
| title | Applied simulation modeling |
| title_auth | Applied simulation modeling |
| title_exact_search | Applied simulation modeling |
| title_full | Applied simulation modeling Andrew F. Seila ; Vlatko Ceric ; Pandu Tadikamalla |
| title_fullStr | Applied simulation modeling Andrew F. Seila ; Vlatko Ceric ; Pandu Tadikamalla |
| title_full_unstemmed | Applied simulation modeling Andrew F. Seila ; Vlatko Ceric ; Pandu Tadikamalla |
| title_short | Applied simulation modeling |
| title_sort | applied simulation modeling |
| topic | Simulation (DE-588)4055072-2 gnd Computersimulation (DE-588)4148259-1 gnd Anwendung (DE-588)4196864-5 gnd Modellierung (DE-588)4170297-9 gnd |
| topic_facet | Simulation Computersimulation Anwendung Modellierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010705725&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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