Process Simulation Using Witness » książka
Process Simulation Using Witness
ISBN-13: 9780470371695 / Angielski / Twarda / 2015 / 592 str.
Process Simulation Using Witness
ISBN-13: 9780470371695 / Angielski / Twarda / 2015 / 592 str.
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Teaches basic and advanced modeling and simulation techniques to both undergraduate and postgraduate students and serves as a practical guide and manual for professionals learning how to build simulation models using WITNESS, a free-standing software package. This book discusses the theory behind simulation and demonstrates how to build simulation models with WITNESS. The book begins with an explanation of the concepts of simulation modeling and a "guided tour" of the WITNESS modeling environment. Next, the authors cover the basics of building simulation models using WITNESS and modeling of material-handling systems. After taking a brief tour in basic probability and statistics, simulation model input analysis is then examined in detail, including the importance and techniques of fitting closed-form distributions to observed data. Next, the authors present simulation output analysis including determining run controls and statistical analysis of simulation outputs and show how to use these techniques and others to undertake simulation model verification and validation. Effective techniques for managing a simulation project are analyzed, and case studies exemplifying the use of simulation in manufacturing and services are covered. Simulation-based optimization methods and the use of simulation to build and enhance lean systems are then discussed. Finally, the authors examine the interrelationships and synergy between simulation and Six Sigma.
- Emphasizes real-world applications of simulation modeling in both services and manufacturing sectors
- Discusses the role of simulation in Six Sigma projects and Lean Systems
- Contains examples in each chapter on the methods and concepts presented
About the Companion Website xvii
Preface xix
Acknowledgments xxiii
1 Concepts of Simulation Modeling 1
1.1 Overview 1
1.2 System Modeling 2
1.2.1 System Concept 2
1.2.2 Modeling Concept 4
1.2.3 Types of Models 5
1.3 Simulation Modeling 11
1.3.1 Simulation Defined 11
1.3.2 Simulation Taxonomy 12
1.4 The Role of Simulation 15
1.4.1 Simulation Justified 15
1.4.2 Simulation Applications 16
1.4.3 Simulation Precautions 17
1.5 Simulation Methodology 20
1.5.1 Identify Problem/Opportunity 20
1.5.2 Develop Solution/Improvement Alternatives 21
1.5.3 Evaluate Solution Alternatives 21
1.5.4 Select the Best Alternative 22
1.5.5 Implement the Selected Alternative 22
1.6 Steps in a Simulation Study 22
1.6.1 Problem Formulation 23
1.6.2 Setting Study Objectives 23
1.6.3 Conceptual Modeling 25
1.6.4 Data Collection 26
1.6.5 Model Building 27
1.6.6 Model Verification 30
1.6.7 Model Validation 30
1.6.8 Model Analysis 31
1.6.9 Study Documentation 32
1.7 Simulation Software 34
1.7.1 WITNESS® Simulation Software 35
1.8 Summary 36
Questions and Exercises 37
Bibliography 38
2 World–Views of Simulation 41
2.1 Overview 41
2.2 System Modeling with DES 42
2.2.1 System Structure 42
2.2.2 System Layout 43
2.2.3 System Data 43
2.2.4 System Logic 44
2.2.5 System Statistics 45
2.3 Elements of Discrete Event Simulation (DES) 45
2.3.1 System Entities (EN) 45
2.3.2 System State (S) 46
2.3.3 State Variables (VR) 46
2.3.4 System Events (E) 47
2.3.5 System Activities (A) 48
2.3.6 System Resources (R) 48
2.3.7 System Delay (D) 50
2.3.8 System Logic (L) 50
2.4 DES Functionality 51
2.4.1 Discrete–Event Mechanism 52
2.4.2 Time–Advancement Mechanism 54
2.4.3 Random Sampling Mechanism 55
2.4.4 Statistical Accumulation Mechanism 58
2.4.5 Animation Mechanism 59
2.5 Example of DES Mechanisms 60
2.6 Monte Carlo Simulation (MCS) 65
2.7 Continuous Simulation 68
2.7.1 WITNESS® for Continuous Simulation 69
2.7.2 Hybrid Simulation 69
2.8 WITNESS® World–views of Simulation 70
2.8.1 Attribute 72
2.8.2 Buffer 72
2.8.3 Carrier 72
2.8.4 Conveyor 73
2.8.5 Fluid 73
2.8.6 Labor 74
2.8.7 Machine 74
2.8.8 Part 75
2.8.9 Path 75
2.8.10 Pipe 75
2.8.11 Processor 75
2.8.12 Sections 75
2.8.13 Station 76
2.8.14 Tank 76
2.8.15 Track 76
2.8.16 Vehicle 76
2.9 Summary 77
Questions and Exercises 78
Bibliography 80
3 WITNESS® Environment 83
3.1 Overview 83
3.2 The WITNESS® Environment 83
3.3 Menus 85
3.3.1 General Menu Operation 86
3.4 Tool Bars 86
3.4.1 Standard Tool Bar 86
3.4.2 Views Toolbar 87
3.4.3 Element Tool Bar 89
3.4.4 Model Tool Bar, 92
3.4.5 Assistant Toolbar, 92
3.4.6 Run Toolbar, 93
3.4.7 Reporting Toolbar, 95
3.4.8 Display Edit Toolbar, 96
3.4.9 Creating a New Toolbar, 99
3.5 Dialog Boxes and Property Sheets 100
3.5.1 Entry/Field Types 100
3.6 Windows 102
3.7 Layers 103
3.8 The WITNESS® Editor 103
3.8.1 Editor Features 103
3.8.2 Manipulating a Window 105
3.9 Window Operations 105
3.9.1 Windows Options 105
3.9.2 The Interact Box 106
3.9.3 The Clock (Time) 107
3.9.4 The Analog Clock 107
3.9.5 Copying, Cutting, and Pasting 107
3.9.6 Copy and Cut Element s Display or Detail Features 108
3.10 The Help Facility 108
3.11 The Basic Elements 109
Questions and Exercises 109
Bibliography 110
4 Basic WITNESS® Modeling Techniques 111
4.1 Overview 111
4.2 Step–by–Step Model Building 111
4.3 Modeling a Simple Manufacturing Process 112
4.3.1 Define: Specifying Elements of the Manufacturing
Process Simulation Model 114
4.3.2 Detail: Adding Specifications for Elements to the Model 114
4.3.3 Display: Modifying the Appearance of Elements in the Layout Window 118
4.4 Modeling a Service Process 126
4.4.1 Service Model Example 126
4.5 WITNESS® Code 141
4.6 An Extended Example 141
Questions and Exercises 143
Bibliography 146
5 Modeling Material Handling Systems 149
5.1 Overview 149
5.2 Material Handling Systems 149
5.3 Material Handling Systems in WITNESS® 150
5.4 Modeling Conveyors 152
5.5 Modeling Paths for Labor and Parts Transit 156
5.6 Modeling Vehicles and Tracks 161
5.7 Modeling Power–&–Free Systems 167
Questions and Exercises 176
Bibliography 176
6 Basic Probability and Statistics for Simulation 179
6.1 Overview 179
6.2 Random Variables (RVs) 179
6.2.1 Examples of Discrete Random Variables 180
6.2.2 Examples of Continuous Random Variables 181
6.3 Point Estimation 182
6.4 Confidence Intervals for the Population Mean 182
6.5 Confidence Intervals for the Population Variance and Standard Deviation 184
6.6 Sample Size Determination when Estimating Population Mean 185
6.7 Theoretical Probability Distributions 186
6.7.1 The Uniform Distribution 187
6.7.2 The Normal Distribution 187
6.7.3 The Exponential Distribution 190
6.7.4 The Erlang Distribution 190
6.7.5 The Gamma Distribution 192
6.7.6 The Weibull Distribution 193
6.7.7 Triangular Distribution 193
Questions and Exercises 197
Bibliography 198
7 Simulation Input Modeling 199
7.1 Overview 199
7.2 Determining Data Requirements 200
7.3 Methods of Data Collection 202
7.4 Representing Collected Data 211
7.5 Validating Collected Data 213
7.5.1 Filtering the Data from Outliers and Wrong Measures 215
7.5.2 Testing the Data for Independence 215
7.5.3 Testing if Data are Identically Distributed 218
7.6 Fitting Probability Distributions to Collected Data 219
7.6.1 Using Empirical Distributions 225
7.7 WITNESS® Input Modeling 226
7.7.1 WITNESS® RNG 227
7.7.2 Incorporating Collected Data in WITNESS® 229
7.7.3 Using Databases with WITNESS® 233
7.8 Practical Aspects of Input Modeling 234
7.8.1 Example of Input Modeling: Auto Service Center 236
7.8.2 Example of Input Modeling: ER Simulation 243
7.9 Summary 249
Questions and Exercises 249
Bibliography 252
8 Simulation Output Analysis 253
8.1 Overview 253
8.2 Terminating Versus Steady–State Simulation 254
8.2.1 Terminating Simulation 254
8.2.2 Steady–State Simulation 257
8.3 Determining Simulation Run Controls 259
8.3.1 Determining Warm–Up Period 260
8.3.2 Determining Simulation Run Length 263
8.3.3 Determining the Number of Simulation Runs 266
8.4 Variability in Simulation Outputs 267
8.4.1 Variance Reduction Techniques 269
8.5 Simulation Output Analysis 270
8.5.1 Statistical Analysis of Simulation Outputs 272
8.5.2 Experimental Design 285
8.6 Example: Output Analyses of a Clinic Simulation 291
8.7 WITNESS® Modules for Simulation Output Analysis 296
8.7.1 WITNESS® Outputs and Charts 296
8.7.2 WITNESS® Costing 297
8.7.3 WITNESS® Scenario Manager 299
8.7.4 WITNESS® Documentor 299
8.7.5 WITNESS® Optimizer 300
8.8 Summary 300
Questions and Exercises 301
Bibliography 303
9 Model Verification and Validation Techniques 305
9.1 Overview 305
9.2 Model Verification Techniques 306
9.2.1 Verifying Model Inputs 308
9.2.2 Verifying Model Logic 309
9.2.3 Verifying Model Outputs 314
9.3 Model Validation Techniques 314
9.3.1 Validating Model Inputs 316
9.3.2 Validating Model Behavior 318
9.3.3 Validating Model Outputs 319
9.4 Verifying WITNESS® Models 320
9.5 Summary 330
Question and Exercise 330
Bibliography 332
10 Simulation Project Management 331
10.1 Overview 331
10.2 Define the Problem 332
10.2.1 Define the Objectives of the Study 332
10.2.2 List the Specific Issues to Be Addressed 334
10.2.3 Determine the Boundary or Domain of the Study 334
10.2.4 Determine the Level of Detail or Proper Abstraction Level 334
10.2.5 Determine if a Simulation Model is Actually Needed 335
10.2.6 Estimate the Required Resources Needed to Do the Study 335
10.2.7 Perform a Cost–Benefit Analysis 335
10.2.8 Create a Planning Chart of the Proposed Project 336
10.2.9 Write a Formal Proposal 336
10.3 Design the Study 337
10.3.1 Estimate the Life Cycle of the Model 338
10.3.2 List Broad Assumptions 338
10.3.3 Estimate the Number of Models Required 338
10.3.4 Determine the Animation Requirements 338
10.3.5 Select the Tool 339
10.3.6 Determine the Level of Data Available and What Data is Needed 339
10.3.7 Determine the Human Requirements and Skill Levels 339
10.3.8 Determine the Audience (Levels of Management) 340
10.3.9 Identify the Deliverables 340
10.3.10 Determine the Priority of the Study in Relationship to Other Studies 340
10.3.11 Set Milestone Dates 341
10.3.12 Write the Project Functional Specifications 341
10.4 Design the Conceptual Model 341
10.4.1 Decide on Continuous, Discrete, or Combined Modeling 342
10.4.2 Determine the Elements that Drive the System 342
10.4.3 Determine the Entities that Should Represent the System Elements 343
10.4.4 Determine the Level of Detail Needed to Describe the System Components 343
10.4.5 Determine the Graphics Requirements of the Model 343
10.4.6 Identify the Areas That Utilize Special Control Logic 344
10.4.7 Determine How to Collect Statistics in the Model and Communicate Results to the Customer 344
10.5 Formulate Inputs, Assumptions, and Process Definition 344
10.5.1 Specify the Operating Philosophy of the System 345
10.5.2 Describe the Physical Constraints of the System 345
10.5.3 Describe the Creation and Termination of Dynamic Elements 345
10.5.4 Describe the Process in Detail 345
10.5.5 Obtain the Operation Specifications 346
10.5.6 Obtain the Material Handling Specifications 346
10.5.7 List All the Assumptions 346
10.5.8 Analyze the Input Data 346
10.5.9 Specify the Runtime Parameters 347
10.5.10 Write the Detailed Project Functional Specifications 347
10.5.11 Validate the Conceptual Model 347
10.6 Build, Verify, and Validate the Model 348
10.7 Experiment with the Model 348
10.8 Documentation and Presentation 349
10.8.1 Project Book 350
10.8.2 Documentation of Model Input, Code, and Output 350
10.8.3 Project Functional Specifications 350
10.8.4 User Manual 350
10.8.5 Maintenance Manual 351
10.8.6 Discussion and Explanation of Model Results 351
10.8.7 Recommendations for Further Areas of Study 351
10.8.8 Final Project Report and Presentation 351
10.9 Define the Model Life Cycle 352
10.9.1 Construct User–Friendly Model Input and Output Interfaces 353
10.9.2 Determine Model and Training Responsibility 353
10.9.3 Establish Data Integrity and Collection Procedures 354
10.9.4 Perform Field Data Validation Tests 354
10.10 Summary 354
Bibliography 354
11 Manufacturing Simulation Case Studies 357
11.1 Overview 357
11.2 Hybrid Simulation of Titanium Manufacturing Process 358
11.2.1 Model Description 358
11.2.2 Model Assumptions 360
11.2.3 Process Logic 360
11.2.4 Start–up Conditions and Model Run Length 361
11.2.5 Model Input Data 361
11.2.6 Model Outputs 363
11.2.7 The WITNESS® Model 363
11.2.8 Model Verification and Validation 366
11.2.9 Model Experiments 367
11.2.10 Project Results and Conclusions 371
11.3 Paint Capacity Study of an Aviation Company 373
11.3.1 Paint Shop Layout 373
11.3.2 Study Assumptions 373
11.3.3 Data Collection 375
11.3.4 The WITNESS® Model 375
11.3.5 Study Results 375
11.3.6 Throughput Improvement Opportunities 375
11.4 Simulation of a Seamless Pipe Facility 376
11.4.1 Study Objectives Include 377
11.4.2 System Description 379
11.4.3 Input Parameters 379
11.4.4 Schedule Data 381
11.4.5 The WITNESS® Model 381
11.4.6 Base Model Worst–Case Schedule 381
11.4.7 Results Summary 387
11.4.8 Observations Summary 389
11.4.9 Conclusions 393
11.5 Summary 393
Bibliography 393
12 Service Simulation Case Studies 395
12.1 Overview 395
12.2 Elements of Service Systems 396
12.2.1 System Entities 396
12.2.2 Service Providers 396
12.2.3 Customer Service 397
12.2.4 Staff and Human Resources 397
12.2.5 Facility Layout and Physical Structure 397
12.2.6 Operating Policies 398
12.3 Characteristics of Service Systems 398
12.4 Modeling Service Systems 399
12.4.1 Modeling Considerations 399
12.4.2 Model Elements 401
12.4.3 Model Control Factors 401
12.4.4 Model Performance Measures 402
12.5 Applications of Service System Simulation 402
12.5.1 Examples of Service Systems Simulation 403
12.6 Case Studies on Service Systems Simulation 404
12.6.1 Car Wash 404
12.6.2 Harbor Traffic Simulation 406
12.6.3 Bank Simulation Example 409
12.6.4 Clinic Simulation Example 411
12.6.5 Public Service Office Simulation 417
12.7 Summary 423
Bibliography 423
13 Simulation–Based Optimization Methods 425
13.1 Overview 425
13.2 Optimization Approaches in Simulation Studies 426
13.3 Simulation–Based Optimization 427
13.4 WITNESS® Experimenter 429
13.4.1 Comparison of Multiple Alternatives with WITNESS® Experimenter 429
13.4.2 More Advanced Use of the Experimenter 435
13.5 Optimization within the WITNESS® Experimenter 440
13.5.1 Productivity–Cost Tradeoffs Explored with the Experimenter 444
13.6 Summary 447
Questions and Exercises 447
Bibliography 448
14 Simulation for Lean Systems 449
14.1 Overview 449
14.2 Basics of Lean Systems 450
14.2.1 Lean Principles 450
14.2.2 Lean Techniques 453
14.2.3 Value Stream Mapping 454
14.3 Simulation–Based Lean Systems 457
14.3.1 Lean Simulation Example 459
14.4 Lean Using WITNESS® 477
14.5 Summary 485
Question and Exercises 485
Bibliography 487
15 Simulation for Six Sigma 489
15.1 Overview 489
15.2 Six Sigma Quality 490
15.2.1 Six Sigma Capability 493
15.2.2 Determining Process Sigma Rating 494
15.3 Six Sigma Methods 496
15.3.1 DMAIC Process 497
15.3.2 Design for Six Sigma (DFSS) 499
15.4 WITNESS® for Six Sigma 501
15.4.1 Sigma Ratings in WITNESS® 504
15.5 Simulation–Based Six Sigma 520
15.5.1 Simulation–Based DMAIC 520
15.5.2 Simulation–Based DFSS 526
15.5.3 Lean Six Sigma (LSS) 537
15.6 Summary 545
Questions and Exercises 546
Bibliography 547
Appendix 549
Index 553
Raid Al–Aomar is a Simulation Expert and a Professor of Industrial Engineering at in College of Engineering at Abu Dhabi University in the UAE.
Edward J. Williams works at the Production Modeling Corporation in Dearborn, Michigan, and teaches courses in Business Analytics at the University of Michigan – Dearborn.
Onur M. Ülgen is a Professor in the Industrial and Manufacturing Systems Engineering Department at the University of Michigan in Dearborn, Michigan. He is also the President of Production Modeling Corporation, a process simulation company with offices in USA (HQ), Sweden, and India.
Teaches basic and advanced modeling and simulation techniques to both undergraduate and postgraduate students and serves as a practical guide and manual for professionals learning how to build simulation models using WITNESS®, a free–standing software package.
This book discusses the theory behind simulation and demonstrates how to build simulation models with WITNESS®. The book begins with an explanation of the concepts of simulation modeling and a guided tour of the WITNESS® modeling environment. Next, the authors cover the basics of building simulation models using WITNESS® and modeling of material–handling systems. After taking a brief tour in basic probability and statistics, simulation model input analysis is then examined in detail, including the importance and techniques of fitting closed–form distributions to observed data. Next, the authors present simulation output analysis including determining run controls and statistical analysis of simulation outputs and show how to use these techniques and others to undertake simulation model verification and validation. Effective techniques for managing a simulation project are analyzed, and case studies exemplifying the use of simulation in manufacturing and services are covered. Simulation–based optimization methods and the use of simulation to build and enhance lean systems are then discussed. Finally, the authors examine the interrelationships and synergy between simulation and Six Sigma.
- Emphasizes real–world applications of simulation modeling in both services and manufacturing sectors
- Discusses the role of simulation in Six Sigma projects and Lean Systems
- Contains examples in each chapter on the methods and concepts presented
Process Simulation Using WITNESS® is a resource for students, researchers, engineers, management consultants, and simulation trainers.
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