Muutke küpsiste eelistusi

Process Simulation Using WITNESS [Kõva köide]

(Jordan University of Science and Technology), ,
  • Formaat: Hardback, 592 pages, kõrgus x laius x paksus: 241x161x34 mm, kaal: 939 g
  • Ilmumisaeg: 23-Oct-2015
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470371692
  • ISBN-13: 9780470371695
Teised raamatud teemal:
Process Simulation Using WITNESSSuurem pilt
  • Formaat: Hardback, 592 pages, kõrgus x laius x paksus: 241x161x34 mm, kaal: 939 g
  • Ilmumisaeg: 23-Oct-2015
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470371692
  • ISBN-13: 9780470371695
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470371695.html
Märksõnad:
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.
About the Companion Website xvii
Preface xix
Acknowledgments xxiii
1 Concepts of Simulation Modeling 1(40)
1.1 Overview,
1(1)
1.2 System Modeling,
2(9)
1.2.1 System Concept,
2(2)
1.2.2 Modeling Concept,
4(1)
1.2.3 Types of Models,
5(6)
1.3 Simulation Modeling,
11(4)
1.3.1 Simulation Defined,
11(1)
1.3.2 Simulation Taxonomy,
12(3)
1.4 The Role of Simulation,
15(5)
1.4.1 Simulation Justified,
15(1)
1.4.2 Simulation Applications,
16(1)
1.4.3 Simulation Precautions,
17(3)
1.5 Simulation Methodology,
20(2)
1.5.1 Identify Problem/Opportunity,
20(1)
1.5.2 Develop Solution/Improvement Alternatives,
21(1)
1.5.3 Evaluate Solution Alternatives,
21(1)
1.5.4 Select the Best Alternative,
22(1)
1.5.5 Implement the Selected Alternative,
22(1)
1.6 Steps in a Simulation Study,
22(12)
1.6.1 Problem Formulation,
23(1)
1.6.2 Setting Study Objectives,
23(2)
1.6.3 Conceptual Modeling,
25(1)
1.6.4 Data Collection,
26(1)
1.6.5 Model Building,
27(3)
1.6.6 Model Verification,
30(1)
1.6.7 Model Validation,
30(1)
1.6.8 Model Analysis,
31(1)
1.6.9 Study Documentation,
32(2)
1.7 Simulation Software,
34(2)
1.7.1 WITNESS® Simulation Software,
35(1)
1.8 Summary,
36(1)
Questions and Exercises,
37(1)
Bibliography,
38(3)
2 World-Views of Simulation 41(42)
2.1 Overview,
41(1)
2.2 System Modeling with DES,
42(3)
2.2.1 System Structure,
42(1)
2.2.2 System Layout,
43(1)
2.2.3 System Data,
43(1)
2.2.4 System Logic,
44(1)
2.2.5 System Statistics,
45(1)
2.3 Elements of Discrete Event Simulation (DES),
45(6)
2.3.1 System Entities (EN),
45(1)
2.3.2 System State (S),
46(1)
2.3.3 State Variables (VR),
46(1)
2.3.4 System Events (E),
47(1)
2.3.5 System Activities (A),
48(1)
2.3.6 System Resources (R),
48(2)
2.3.7 System Delay (D),
50(1)
2.3.8 System Logic (L),
50(1)
2.4 DES Functionality,
51(9)
2.4.1 Discrete-Event Mechanism,
52(2)
2.4.2 Time-Advancement Mechanism,
54(1)
2.4.3 Random Sampling Mechanism,
55(3)
2.4.4 Statistical Accumulation Mechanism,
58(1)
2.4.5 Animation Mechanism,
59(1)
2.5 Example of DES Mechanisms,
60(5)
2.6 Monte Carlo Simulation (MCS),
65(3)
2.7 Continuous Simulation,
68(2)
2.7.1 WITNESS® for Continuous Simulation,
69(1)
2.7.2 Hybrid Simulation,
69(1)
2.8 WITNESS® World-views of Simulation,
70(7)
2.8.1 Attribute,
72(1)
2.8.2 Buffer,
72(1)
2.8.3 Carrier,
72(1)
2.8.4 Conveyor,
73(1)
2.8.5 Fluid,
73(1)
2.8.6 Labor,
74(1)
2.8.7 Machine,
74(1)
2.8.8 Part,
75(1)
2.8.9 Path,
75(1)
2.8.10 Pipe,
75(1)
2.8.11 Processor,
75(1)
2.8.12 Sections,
75(1)
2.8.13 Station,
76(1)
2.8.14 Tank,
76(1)
2.8.15 Track,
76(1)
2.8.16 Vehicle,
76(1)
2.9 Summary,
77(1)
Questions and Exercises,
78(2)
Bibliography,
80(3)
3 WITNESS® Environment 83(28)
3.1 Overview,
83(1)
3.2 The WITNESS® Environment,
83(2)
3.3 Menus,
85(1)
3.3.1 General Menu Operation,
86(1)
3.4 Tool Bars,
86(14)
3.4.1 Standard Tool Bar,
86(1)
3.4.2 Views Toolbar,
87(2)
3.4.3 Element Tool Bar,
89(3)
3.4.4 Model Tool Bar,
92(1)
3.4.5 Assistant Toolbar,
92(1)
3.4.6 Run Toolbar,
93(2)
3.4.7 Reporting Toolbar,
95(1)
3.4.8 Display Edit Toolbar,
96(3)
3.4.9 Creating a New Toolbar,
99(1)
3.5 Dialog Boxes and Property Sheets,
100(2)
3.5.1 Entry/Field Types,
100(2)
3.6 Windows,
102(1)
3.7 Layers,
103(1)
3.8 The WITNESS® Editor,
103(2)
3.8.1 Editor Features,
103(2)
3.8.2 Manipulating a Window,
105(1)
3.9 Window Operations,
105(3)
3.9.1 Windows Options,
105(1)
3.9.2 The Interact Box,
106(1)
3.9.3 The Clock (Time),
107(1)
3.9.4 The Analog Clock,
107(1)
3.9.5 Copying, Cutting, and Pasting,
107(1)
3.9.6 Copy and Cut Element's Display or Detail Features,
108(1)
3.10 The Help Facility,
108(1)
3.11 The Basic Elements,
109(1)
Questions and Exercises,
109(1)
Bibliography,
110(1)
4 Basic WITNESS® Modeling Techniques 111(38)
4.1 Overview,
111(1)
4.2 Step-by-Step Model Building,
111(1)
4.3 Modeling a Simple Manufacturing Process,
112(14)
4.3.1 Define: Specifying Elements of the Manufacturing Process Simulation Model,
114(1)
4.3.2 Detail: Adding Specifications for Elements to the Model,
114(4)
4.3.3 Display: Modifying the Appearance of Elements in the Layout Window,
118(8)
4.4 Modeling a Service Process,
126(15)
4.4.1 Service Model Example,
126(15)
4.5 WITNESS® Code,
141(1)
4.6 An Extended Example,
141(2)
Questions and Exercises,
143(3)
Bibliography,
146(3)
5 Modeling Material Handling Systems 149(30)
5.1 Overview,
149(1)
5.2 Material Handling Systems,
149(1)
5.3 Material Handling Systems in WITNESS®,
150(2)
5.4 Modeling Conveyors,
152(4)
5.5 Modeling Paths for Labor and Parts Transit,
156(5)
5.6 Modeling Vehicles and Tracks,
161(6)
5.7 Modeling Power-&-Free Systems,
167(9)
Questions and Exercises,
176(1)
Bibliography,
176(3)
6 Basic Probability and Statistics for Simulation 179(20)
6.1 Overview,
179(1)
6.2 Random Variables (RVs),
179(3)
6.2.1 Examples of Discrete Random Variables,
180(1)
6.2.2 Examples of Continuous Random Variables,
181(1)
6.3 Point Estimation,
182(1)
6.4 Confidence Intervals for the Population Mean,
182(2)
6.5 Confidence Intervals for the Population Variance and Standard Deviation,
184(1)
6.6 Sample Size Determination when Estimating Population Mean,
185(1)
6.7 Theoretical Probability Distributions,
186(11)
6.7.1 The Uniform Distribution,
187(1)
6.7.2 The Normal Distribution,
187(3)
6.7.3 The Exponential Distribution,
190(1)
6.7.4 The Erlang Distribution,
190(2)
6.7.5 The Gamma Distribution,
192(1)
6.7.6 The Weibull Distribution,
193(1)
6.7.7 Triangular Distribution,
193(4)
Questions and Exercises,
197(1)
Bibliography,
198(1)
7 Simulation Input Modeling 199(54)
7.1 Overview,
199(1)
7.2 Determining Data Requirements,
200(2)
7.3 Methods of Data Collection,
202(9)
7.4 Representing Collected Data,
211(2)
7.5 Validating Collected Data,
213(6)
7.5.1 Filtering the Data from Outliers and Wrong Measures,
215(1)
7.5.2 Testing the Data for Independence,
215(3)
7.5.3 Testing if Data are Identically Distributed,
218(1)
7.6 Fitting Probability Distributions to Collected Data,
219(7)
7.6.1 Using Empirical Distributions,
225(1)
7.7 WITNESS® Input Modeling,
226(8)
7.7.1 WITNESS® RNG,
227(2)
7.7.2 Incorporating Collected Data in WITNESS®,
229(4)
7.7.3 Using Databases with WITNESS®,
233(1)
7.8 Practical Aspects of Input Modeling,
234(15)
7.8.1 Example of Input Modeling: Auto Service Center,
236(7)
7.8.2 Example of Input Modeling: ER Simulation,
243(6)
7.9 Summary,
249(1)
Questions and Exercises,
249(3)
Bibliography,
252(1)
8 Simulation Output Analysis 253(52)
8.1 Overview,
253(1)
8.2 Terminating Versus Steady-State Simulation,
254(5)
8.2.1 Terminating Simulation,
254(3)
8.2.2 Steady-State Simulation,
257(2)
8.3 Determining Simulation Run Controls,
259(8)
8.3.1 Determining Warm-Up Period,
260(3)
8.3.2 Determining Simulation Run Length,
263(3)
8.3.3 Determining the Number of Simulation Runs,
266(1)
8.4 Variability in Simulation Outputs,
267(3)
8.4.1 Variance Reduction Techniques,
269(1)
8.5 Simulation Output Analysis,
270(21)
8.5.1 Statistical Analysis of Simulation Outputs,
272(13)
8.5.2 Experimental Design,
285(6)
8.6 Example: Output Analyses of a Clinic Simulation,
291(5)
8.7 WITNESS® Modules for Simulation Output Analysis,
296(4)
8.7.1 WITNESS® Outputs and Charts,
296(1)
8.7.2 WITNESS® Costing,
297(2)
8.7.3 WITNESS® Scenario Manager,
299(1)
8.7.4 WITNESS® Documentor,
299(1)
8.7.5 WITNESS® Optimizer,
300(1)
8.8 Summary,
300(1)
Questions and Exercises,
301(2)
Bibliography,
303(2)
9 Model Verification and Validation Techniques 305(26)
9.1 Overview,
305(1)
9.2 Model Verification Techniques,
306(8)
9.2.1 Verifying Model Inputs,
308(1)
9.2.2 Verifying Model Logic,
309(5)
9.2.3 Verifying Model Outputs,
314(1)
9.3 Model Validation Techniques,
314(6)
9.3.1 Validating Model Inputs,
316(2)
9.3.2 Validating Model Behavior,
318(1)
9.3.3 Validating Model Outputs,
319(1)
9.4 Verifying WITNESS® Models,
320(10)
9.5 Summary,
330(1)
Question and Exercise,
330(2)
Bibliography,
332
10 Simulation Project Management 331(26)
10.1 Overview,
331(1)
10.2 Define the Problem,
332(5)
10.2.1 Define the Objectives of the Study,
332(2)
10.2.2 List the Specific Issues to Be Addressed,
334(1)
10.2.3 Determine the Boundary or Domain of the Study,
334(1)
10.2.4 Determine the Level of Detail or Proper Abstraction Level,
334(1)
10.2.5 Determine if a Simulation Model is Actually Needed,
335(1)
10.2.6 Estimate the Required Resources Needed to Do the Study,
335(1)
10.2.7 Perform a Cost-Benefit Analysis,
335(1)
10.2.8 Create a Planning Chart of the Proposed Project,
336(1)
10.2.9 Write a Formal Proposal,
336(1)
10.3 Design the Study,
337(4)
10.3.1 Estimate the Life Cycle of the Model,
338(1)
10.3.2 List Broad Assumptions,
338(1)
10.3.3 Estimate the Number of Models Required,
338(1)
10.3.4 Determine the Animation Requirements,
338(1)
10.3.5 Select the Tool,
339(1)
10.3.6 Determine the Level of Data Available and What Data is Needed,
339(1)
10.3.7 Determine the Human Requirements and Skill Levels,
339(1)
10.3.8 Determine the Audience (Levels of Management),
340(1)
10.3.9 Identify the Deliverables,
340(1)
10.3.10 Determine the Priority of the Study in Relationship to Other Studies,
340(1)
10.3.11 Set Milestone Dates,
341(1)
10.3.12 Write the Project Functional Specifications,
341(1)
10.4 Design the Conceptual Model,
341(3)
10.4.1 Decide on Continuous, Discrete, or Combined Modeling,
342(1)
10.4.2 Determine the Elements that Drive the System,
342(1)
10.4.3 Determine the Entities that Should Represent the System Elements,
343(1)
10.4.4 Determine the Level of Detail Needed to Describe the System Components,
343(1)
10.4.5 Determine the Graphics Requirements of the Model,
343(1)
10.4.6 Identify the Areas That Utilize Special Control Logic,
344(1)
10.4.7 Determine How to Collect Statistics in the Model and Communicate Results to the Customer,
344(1)
10.5 Formulate Inputs, Assumptions, and Process Definition,
344(4)
10.5.1 Specify the Operating Philosophy of the System,
345(1)
10.5.2 Describe the Physical Constraints of the System,
345(1)
10.5.3 Describe the Creation and Termination of Dynamic Elements,
345(1)
10.5.4 Describe the Process in Detail,
345(1)
10.5.5 Obtain the Operation Specifications,
346(1)
10.5.6 Obtain the Material Handling Specifications,
346(1)
10.5.7 List All the Assumptions,
346(1)
10.5.8 Analyze the Input Data,
346(1)
10.5.9 Specify the Runtime Parameters,
347(1)
10.5.10 Write the Detailed Project Functional Specifications,
347(1)
10.5.11 Validate the Conceptual Model,
347(1)
10.6 Build, Verify, and Validate the Model,
348(1)
10.7 Experiment with the Model,
348(1)
10.8 Documentation and Presentation,
349(3)
10.8.1 Project Book,
350(1)
10.8.2 Documentation of Model Input, Code, and Output,
350(1)
10.8.3 Project Functional Specifications,
350(1)
10.8.4 User Manual,
350(1)
10.8.5 Maintenance Manual,
351(1)
10.8.6 Discussion and Explanation of Model Results,
351(1)
10.8.7 Recommendations for Further Areas of Study,
351(1)
10.8.8 Final Project Report and Presentation,
351(1)
10.9 Define the Model Life Cycle,
352(2)
10.9.1 Construct User-Friendly Model Input and Output Interfaces,
353(1)
10.9.2 Determine Model and Training Responsibility,
353(1)
10.9.3 Establish Data Integrity and Collection Procedures,
354(1)
10.9.4 Perform Field Data Validation Tests,
354(1)
10.10 Summary,
354(1)
Bibliography,
354(3)
11 Manufacturing Simulation Case Studies 357(38)
11.1 Overview,
357(1)
11.2 Hybrid Simulation of Titanium Manufacturing Process,
358(15)
11.2.1 Model Description,
358(2)
11.2.2 Model Assumptions,
360(1)
11.2.3 Process Logic,
360(1)
11.2.4 Start-up Conditions and Model Run Length,
361(1)
11.2.5 Model Input Data,
361(2)
11.2.6 Model Outputs,
363(1)
11.2.7 The WITNESS® Model,
363(3)
11.2.8 Model Verification and Validation,
366(1)
11.2.9 Model Experiments,
367(4)
11.2.10 Project Results and Conclusions,
371(2)
11.3 Paint Capacity Study of an Aviation Company,
373(3)
11.3.1 Paint Shop Layout,
373(1)
11.3.2 Study Assumptions,
373(2)
11.3.3 Data Collection,
375(1)
11.3.4 The WITNESS® Model,
375(1)
11.3.5 Study Results,
375(1)
11.3.6 Throughput Improvement Opportunities,
375(1)
11.4 Simulation of a Seamless Pipe Facility,
376(17)
11.4.1 Study Objectives Include,
377(2)
11.4.2 System Description,
379(1)
11.4.3 Input Parameters,
379(2)
11.4.4 Schedule Data,
381(1)
11.4.5 The WITNESS® Model,
381(1)
11.4.6 Base Model—Worst-Case Schedule,
381(6)
11.4.7 Results Summary,
387(2)
11.4.8 Observations Summary,
389(4)
11.4.9 Conclusions,
393(1)
11.5 Summary,
393(1)
Bibliography,
393(2)
12 Service Simulation Case Studies 395(30)
12.1 Overview,
395(1)
12.2 Elements of Service Systems,
396(2)
12.2.1 System Entities,
396(1)
12.2.2 Service Providers,
396(1)
12.2.3 Customer Service,
397(1)
12.2.4 Staff and Human Resources,
397(1)
12.2.5 Facility Layout and Physical Structure,
397(1)
12.2.6 Operating Policies,
398(1)
12.3 Characteristics of Service Systems,
398(1)
12.4 Modeling Service Systems,
399(3)
12.4.1 Modeling Considerations,
399(2)
12.4.2 Model Elements,
401(1)
12.4.3 Model Control Factors,
401(1)
12.4.4 Model Performance Measures,
402(1)
12.5 Applications of Service System Simulation,
402(2)
12.5.1 Examples of Service Systems Simulation,
403(1)
12.6 Case Studies on Service Systems Simulation,
404(19)
12.6.1 Car Wash,
404(2)
12.6.2 Harbor Traffic Simulation,
406(3)
12.6.3 Bank Simulation Example,
409(2)
12.6.4 Clinic Simulation Example,
411(6)
12.6.5 Public Service Office Simulation,
417(6)
12.7 Summary,
423(1)
Bibliography,
423(2)
13 Simulation-Based Optimization Methods 425(24)
13.1 Overview,
425(1)
13.2 Optimization Approaches in Simulation Studies,
426(1)
13.3 Simulation-Based Optimization,
427(2)
13.4 WITNESS® Experimenter,
429(11)
13.4.1 Comparison of Multiple Alternatives with WITNESS® Experimenter,
429(6)
13.4.2 More Advanced Use of the Experimenter,
435(5)
13.5 Optimization within the WITNESS® Experimenter,
440(7)
13.5.1 Productivity-Cost Tradeoffs Explored with the Experimenter,
444(3)
13.6 Summary,
447(1)
Questions and Exercises,
447(1)
Bibliography,
448(1)
14 Simulation for Lean Systems 449(40)
14.1 Overview,
449(1)
14.2 Basics of Lean Systems,
450(7)
14.2.1 Lean Principles,
450(3)
14.2.2 Lean Techniques,
453(1)
14.2.3 Value Stream Mapping,
454(3)
14.3 Simulation-Based Lean Systems,
457(20)
14.3.1 Lean Simulation Example,
459(18)
14.4 Lean Using WITNESS®,
477(8)
14.5 Summary,
485(1)
Question and Exercises,
485(2)
Bibliography,
487(2)
15 Simulation for Six Sigma 489(60)
15.1 Overview,
489(1)
15.2 Six Sigma Quality,
490(6)
15.2.1 Six Sigma Capability,
493(1)
15.2.2 Determining Process Sigma Rating,
494(2)
15.3 Six Sigma Methods,
496(5)
15.3.1 DMAIC Process,
497(2)
15.3.2 Design for Six Sigma (DFSS),
499(2)
15.4 WITNESS® for Six Sigma,
501(19)
15.4.1 Sigma Ratings in WITNESS®,
504(16)
15.5 Simulation-Based Six Sigma,
520(25)
15.5.1 Simulation-Based DMAIC,
520(6)
15.5.2 Simulation-Based DFSS,
526(11)
15.5.3 Lean Six Sigma (LSS),
537(8)
15.6 Summary,
545(1)
Questions and Exercises,
546(1)
Bibliography,
547(2)
Appendix 549(4)
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.