Details

Process Simulation Using WITNESS

1. Aufl.

von: Raid Al-Aomar, Edward J. Williams, Onur M. Ulgen
127,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	11.08.2015
ISBN/EAN:	9781119019756
Sprache:	englisch
Anzahl Seiten:	592

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Beschreibungen

Titelbeschreibung

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. <ul> <li>Emphasizes real-world applications of simulation modeling in both services and manufacturing sectors</li> <li>Discusses the role of simulation in Six Sigma projects and Lean Systems</li> <li>Contains examples in each chapter on the methods and concepts presented</li> </ul> Process Simulation Using WITNESS is a resource for students, researchers, engineers, management consultants, and simulation trainers.

Inhaltsverzeichnis

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

Autorenportrait

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.

Back cover copy

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. <ul> <li>Emphasizes real-world applications of simulation modeling in both services and manufacturing sectors</li> <li>Discusses the role of simulation in Six Sigma projects and Lean Systems</li> <li>Contains examples in each chapter on the methods and concepts presented</li> </ul> Process Simulation Using WITNESS® is a resource for students, researchers, engineers, management consultants, and simulation trainers.