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E-raamat: Modeling and Simulation of Discrete-Event Systems [Wiley Online]

(Department of Industrial and Systems Engineering, KAIST, South Korea; Department of Computer Science, KAU, Saudi Arabia), (Department of Industrial and Systems Engineering, KAIST, South Korea)
  • Formaat: 432 pages
  • Ilmumisaeg: 08-Nov-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118732790
  • ISBN-13: 9781118732793
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  • Wiley Online
  • Hind: 130,05 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Modeling and Simulation of Discrete-Event SystemsSuurem pilt
  • Formaat: 432 pages
  • Ilmumisaeg: 08-Nov-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118732790
  • ISBN-13: 9781118732793
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118732793
Computer modeling and simulation (M&S) allows engineers to study and analyze complex systems. Discrete-event system (DES)-M&S is used in modern management, industrial engineering, computer science, and the military. As computer speeds and memory capacity increase, so DES-M&S tools become more powerful and more widely used in solving real-life problems.

Based on over 20 years of evolution within a classroom environment, as well as on decades-long experience in developing simulation-based solutions for high-tech industries, Modeling and Simulation of Discrete-Event Systems is the only book on DES-M&S in which all the major DES modeling formalisms activity-based, process-oriented, state-based, and event-based are covered in a unified manner:





A well-defined procedure for building a formal model in the form of event graph, ACD, or state graph Diverse types of modeling templates and examples that can be used as building blocks for a complex, real-life model A systematic, easy-to-follow procedure combined with sample C# codes for developing simulators in various modeling formalisms Simple tutorials as well as sample model files for using popular off-the-shelf simulators such as SIGMA®, ACE®, and Arena® Up-to-date research results as well as research issues and directions in DES-M&S

Modeling and Simulation of Discrete-Event Systems is an ideal textbook for undergraduate and graduate students of simulation/industrial engineering and computer science, as well as for simulation practitioners and researchers.
Preface xvii
Abbreviations xix
Part I Basics Of System Modeling And Simulation 1(42)
1 Overview of Computer Simulation
3(14)
1.1 Introduction
3(1)
1.2 What Is a System?
4(2)
1.2.1 Definitions of Systems
4(1)
1.2.2 Three Types of Systems
4(1)
1.2.3 System Boundaries and Hierarchical Structure
5(1)
1.3 What Is Computer Simulation?
6(3)
1.3.1 What Is Simulation?
6(1)
1.3.2 Why Simulate?
7(1)
1.3.3 Types of Computer Simulation
8(1)
1.4 What Is Discrete-Event Simulation?
9(2)
1.4.1 Description of System Dynamics
10(1)
1.4.2 Simulation Model Trajectory
10(1)
1.4.3 Collecting Statistics from the Model Trajectory
11(1)
1.5 What Is Continuous Simulation?
11(1)
1.5.1 Manual Simulation of the Newtonian Cooling Model
12(1)
1.5.2 Simulation of the Newtonian Cooling Model Using a Simulator
12(1)
1.6 What Is Monte Carlo Simulation?
12(3)
1.6.1 Numerical Integration via Monte Carlo Simulation
12(2)
1.6.2 Risk Analysis via Monte Carlo Simulation
14(1)
1.7 What Are Simulation Experimentation and Optimization?
15(1)
1.8 Review Questions
16(1)
2 Basics of Discrete-Event System Modeling and Simulation
17(26)
2.1 Introduction
17(1)
2.2 How Is a Discrete-Event Simulation Carried Out?
17(6)
2.2.1 Event Routines
18(1)
2.2.2 Simulation Model Trajectory
19(1)
2.2.3 Manual Simulation Execution
19(3)
2.2.4 Flow Chart of Manual Simulation Procedure
22(1)
2.3 Framework of Discrete-Event System Modeling
23(9)
2.3.1 What Are Modeling Components and Reference Model?
23(1)
2.3.2 What Is a Discrete-Event System (DES) Modeling Formalism?
24(2)
2.3.3 What Is a Formal Model and How Is It Specified?
26(3)
2.3.4 Integrated Framework of DES Modeling
29(3)
2.4 Illustrative Examples of DES Modeling and Simulation
32(6)
2.4.1 How to Build and Simulate an Event Graph Model of a DES
33(2)
2.4.2 How to Build and Simulate an ACD Model of a DES
35(2)
2.4.3 How to Build and Simulate a State Graph Model of a DES
37(1)
2.5 Application Frameworks for Discrete-Event System Modeling and Simulation
38(2)
2.5.1 How Is the M&S Life Cycle Managed?
38(1)
2.5.2 Framework for Factory Life-Cycle Support
39(1)
2.6 What to Cover in a Simulation Class
40(2)
2.6.1 Event-Based M&S and Event-Graph Simulation with SIGMA®
41(1)
2.6.2 Activity-Based M&S and Hands-On Modeling Practice with Arena®
41(1)
2.6.3 State-Based M&S
41(1)
2.7 Review Questions
42(1)
Part II Fundamentals Of Discrete-Event System Modeling And Simulation 43(210)
3 Input Modeling for Simulation
45(24)
3.1 Introduction
45(1)
3.2 Empirical Input Modeling
46(2)
3.2.1 Nonparametric Modeling
46(1)
3.2.2 Empirical Modeling of Individual Data
46(1)
3.2.3 Empirical Modeling of Grouped Data
47(1)
3.3 Overview of Theoretical Distribution Fitting
48(2)
3.3.1 Data Independence Checking
48(1)
3.3.2 Distribution Function Selection
49(1)
3.3.3 Parameter Estimation
49(1)
3.3.4 Goodness-of-Fit Test
49(1)
3.3.5 Overview of Random Variate Generation
49(1)
3.4 Theoretical Modeling of Arrival Processes
50(3)
3.4.1 Theoretical Basis for Arrival Process Modeling
50(1)
3.4.2 Generation of Inter-Arrival Times for a Constant Arrival Rate
51(1)
3.4.3 Generation of Inter-Arrival Times for Varying Arrival Rates
52(1)
3.5 Theoretical Modeling of Service Times
53(4)
3.5.1 Generation of Service Time in the Absence of Data
53(2)
3.5.2 Generation of Service Times from Collected Data
55(2)
3.6 Input Modeling for Special Applications
57(2)
3.6.1 Interfailure Time Modeling
57(1)
3.6.2 Inspection Process Modeling
58(1)
3.6.3 Batch Size Modeling
59(1)
3.7 Review Questions
59(1)
Appendix 3A: Parameter Estimation
60 (4)
3A.1 Exponential Distribution
60(1)
3A.2 Erlang Distribution
60(1)
3A.3 Beta Distribution
61(1)
3A.4 Weibull Distribution
62(2)
3A.5 Normal and Lognormal Distributions
64(1)
Appendix 3B: Random Variate Generation
64(5)
3B.1 Exponential Random Variate
64(1)
3B.2 Erlang Random Variate
65(1)
3B.3 Beta Random Variate
65(1)
3B.4 Weibull Random Variate
66(1)
3B.5 Normal and Lognormal Random Variates
67(1)
3B.6 Triangular Random Variate
67(2)
4 Introduction to Event-Based Modeling and Simulation
69(38)
4.1 Introduction
69(1)
4.2 Modeling and Simulation of a Single Server System
70(2)
4.2.1 Reference Modeling
70(1)
4.2.2 Formal Modeling
71(1)
4.2.3 Model Execution
72(1)
4.3 Execution Rules and Specifications of Event Graph Models
72(3)
4.3.1 Event Graph Execution Rules
72(1)
4.3.2 Tabular Specification of Event Graph Models
73(2)
4.3.3 Algebraic Specifications of an Event Graph Model
75(1)
4.4 Event Graph Modeling Templates
75(7)
4.4.1 Single Queue Models
76(4)
4.4.2 Tandem Line Models
80(2)
4.5 Event Graph Modeling Examples
82(9)
4.5.1 Flexible Multi-Server System with Fluctuating Arrival Rates
82(1)
4.5.2 Car Repair Shop
82(2)
4.5.3 Project Management Modeling
84(1)
4.5.4 Conveyor-Driven Serial Line
85(1)
4.5.5 Inline-Type Manufacturing Cell Modeling
86(5)
4.6 Execution of Event Graph Models with SIGMA
91(8)
4.6.1 Simulation of a Single Server System with SIGMA
92(3)
4.6.2 Simulation of a Conveyor-Driven Serial Line with SIGMA
95(4)
4.7 Developing Your Own Event Graph Simulator
99(7)
4.7.1 Functions for Handling Events and Managing Queues
99(2)
4.7.2 Functions for Generating Random Variates
101(1)
4.7.3 Event Routines
101(1)
4.7.4 Next Event Methodology of Simulation Execution
102(1)
4.7.5 Single Server System Simulator
103(3)
4.8 Review Questions
106(1)
5 Parameterized Event Graph Modeling and Simulation
107(36)
5.1 Introduction
107(1)
5.2 Parameterized Event Graph Examples
108(2)
5.2.1 Introducing Index Variables to a Repeating Event-Vertex Pattern
108(1)
5.2.2 Passing Attribute Values of Each Entity along Event Vertices
109(1)
5.3 Execution Rules and Specifications of the Parameterized Event Graph
110(2)
5.3.1 Execution Rules of the PEG Model
110(1)
5.3.2 Tabular Specifications of the PEG Model
110(1)
5.3.3 Algebraic Specifications of the PEG Model
111(1)
5.4 Parameterized Event Graph Modeling of Tandem Lines
112(3)
5.4.1 PEG Modeling of an Unlimited Buffer Tandem Line
112(1)
5.4.2 PEG Modeling of a Limited Buffer Tandem Line
113(1)
5.4.3 PEG Modeling of a Conveyor-Driven Serial Line
114(1)
5.5 Parameterized Event Graph Modeling of Job Shops
115(7)
5.5.1 PEG Modeling of a Simple Job Shop without Transport
115(2)
5.5.2 PEG Modeling of a Job Shop with Transport and Setup Times
117(1)
5.5.3 PEG Modeling of an Inline Job Shop
118(3)
5.5.4 PEG Modeling of a Mixed Job Shop
121(1)
5.6 Execution of Parameterized Event Graph Models Using SIGMA
122(15)
5.6.1 Collecting Sojourn Time Statistics Using SIGMA Functions
123(3)
5.6.2 Simulating a Simple Service Shop with SIGMA
126(2)
5.6.3 Simulation of a Three-Stage Tandem Line Using SIGMA
128(3)
5.6.4 Simulation of the Simple Job Shop with SIGMA
131(6)
5.7 Developing Your Own Parameterized Event Graph Simulator
137(5)
5.7.1 Tandem Line PEG Simulator
137(3)
5.7.2 Simple Job Shop PEG Simulator
140(2)
5.8 Review Questions
142(1)
6 Introduction to Activity-Based Modeling and Simulation
143(41)
6.1 Introduction
143(2)
6.2 Definitions and Specifications of an Activity Cycle Diagram
145(5)
6.2.1 Definitions of an ACD
146(1)
6.2.2 Execution Rules and Tabular Specifications of an ACD
147(1)
6.2.3 Algebraic Specifications of an ACD
148(2)
6.3 Activity Cycle Diagram Modeling Templates
150(6)
6.3.1 ACD Template for Flexible Multi-Server System Modeling
151(1)
6.3.2 ACD Template for Limited Buffer Tandem Line Modeling
152(1)
6.3.3 ACD Template for Nonstationary Arrival Process
153(1)
6.3.4 ACD Template for Batched Service Modeling
153(1)
6.3.5 ACD Template for Joining Operation Modeling
154(1)
6.3.6 ACD Template for Probabilistic Branching Modeling
154(1)
6.3.7 ACD Template for Resource Failure Modeling
155(1)
6.4 Activity-Based Modeling Examples
156(7)
6.4.1 Activity-Based Modeling of a Worker-Operated Tandem Line
156(1)
6.4.2 Activity-Based Modeling of an Inspection-Repair Line
157(1)
6.4.3 Activity-Based Modeling of a Restaurant
158(1)
6.4.4 Activity-Based Modeling of a Simple Service Station
159(1)
6.4.5 Activity-Based Modeling of a Car Repair Shop
160(1)
6.4.6 Activity-Based Modeling of a Project Management System
161(1)
6.4.7 Activity-Based Modeling of a Conveyor-Driven Serial Line
161(2)
6.5 Parameterized Activity Cycle Diagram and Its Application
163(8)
6.5.1 Definition and Specifications of Parameterized ACD
163(1)
6.5.2 Rules for Executing the P-ACD Model
164(1)
6.5.3 P-ACD Modeling of Tandem Lines
165(3)
6.5.4 P-ACD Modeling of Job Shops
168(3)
6.6 Execution of Activity Cycle Diagram Models with a Formal Simulator ACE®
171(12)
6.6.1 Simulation of Single Server Model with ACE
171(4)
6.6.2 Simulation of Probabilistic Branching Model with ACE
175(1)
6.6.3 Simulation of Resource Failure Model with ACE
176(4)
6.6.4 Simulation of Simple Service Station Model with ACE
180(3)
6.7 Review Questions
183(1)
7 Simulation of ACD Models Using Arena®
184(40)
7.1 Introduction
184(1)
7.2 Arena Basics
185(12)
7.2.1 Arena Modeling Environment
186(1)
7.2.2 Building a Flowchart Model of a Process-Inspect Line
187(4)
7.2.3 Completing a Static Model of a Process-Inspect Line
191(1)
7.2.4 Arena Simulation and Output Reports
192(2)
7.2.5 Arena Modules
194(3)
7.3 Activity Cycle Diagram-to-Arena Conversion Templates
197(12)
7.3.1 Template for Fixed Multi-Server Modeling
198(3)
7.3.2 Template for Flexible Multi-Server Modeling
201(1)
7.3.3 Template for Balking (Conditional Branching) Modeling
202(2)
7.3.4 Template for Limited Buffer Tandem Line Modeling
204(1)
7.3.5 Template for Nonstationary Arrival Process Modeling
205(1)
7.3.6 Template for Joining Operation Modeling
206(1)
7.3.7 Template for Inspection (Probabilistic Branching) Modeling
207(1)
7.3.8 Template for Resource Failure Modeling
208(1)
7.4 Activity Cycle Diagram-Based Arena Modeling Examples
209(14)
7.4.1 ACD-Based Arena Modeling of a Worker-Operated Tandem Line
210(1)
7.4.2 ACD-Based Arena Modeling of Restaurant
211(2)
7.4.3 ACD-Based Arena Modeling of a Simple Service Station
213(1)
7.4.4 ACD-Based Arena Modeling of a Project Management System
214(2)
7.4.5 ACD-Based Arena Modeling of a Job Shop
216(3)
7.4.6 ACD-Based Arena Modeling of a Conveyor-Driven Serial Line
219(4)
7.5 Review Questions
223(1)
8 Output Analysis and Optimization
224(29)
8.1 Introduction
224(1)
8.2 Framework of Simulation Output Analyses
225(3)
8.2.1 Verification and Calibration
225(1)
8.2.2 Simulation Experimentation
226(1)
8.2.3 Communication and Presentation
227(1)
8.3 Qualitative Output Analyses
228(2)
8.4 Statistical Output Analyses
230(4)
8.4.1 Statistical Output Analyses for Terminating Simulations
230(1)
8.4.2 Statistical Output Analyses for Nonterminating Simulations
231(2)
8.4.3 Statistical Output Analyses for Comparing Alternative Systems
233(1)
8.5 Linear Regression Modeling for Output Analyses
234(7)
8.5.1 Linear Regression Models
234(1)
8.5.2 Regression Parameter Estimation
235(1)
8.5.3 Test for Significance of Regression
236(2)
8.5.4 Linear Regression Modeling Example
238(2)
8.5.5 Regression Model Fitting for Qualitative Variables
240(1)
8.6 Response Surface Methodology for Simulation Optimization
241(6)
8.6.1 Overview of RSM for Process Optimization
241(1)
8.6.2 Searching for Optimum Regions with the Steepest Ascent
241(4)
8.6.3 Second-Order Model Fitting for Optimization
245(2)
8.7 Review Questions
247(1)
Appendix 8A: Student's t-Distribution
248(1)
8A.1 Definition
248(1)
8A.2 Derivation of the t-Statistic
248(1)
8A.3 Table of Critical t-Values with Degrees of Freedom (df)
248(1)
Appendix 8B: Student's t-Tests
249(6)
8B.1 One Sample t-Test
249(1)
8B.2 Unpaired Two Sample t-Test
250 (3)
Part III Advances In Discrete-Event System Modeling And Simulation 253(142)
9 State-Based Modeling and Simulation
255(44)
9.1 Introduction
255(1)
9.2 Finite State Machine
256(5)
9.2.1 Existing Definitions of Finite State Machines
256(1)
9.2.2 Finite State Machine Models
257(1)
9.2.3 Finite State Machine Modeling of Buffer Storage and Single Server Systems
258(1)
9.2.4 Execution of Finite State Machine Models
259(2)
9.3 Timed Automata
261(6)
9.3.1 Language and Automata
261(1)
9.3.2 Timed Automata
262(1)
9.3.3 Timed Automata with Guards
263(3)
9.3.4 Networks of Timed Automata
266(1)
9.4 State Graphs
267(4)
9.4.1 State Variables and Macro States
267(1)
9.4.2 Timers and System Variables
268(1)
9.4.3 Conventions for Building State Graphs and State Transition Tables
269(2)
9.5 System Modeling with State Graph
271(12)
9.5.1 State Graph Modeling of Dining Philosophers
271(1)
9.5.2 State Graph Modeling of a Table Tennis Game
272(3)
9.5.3 State Graph Modeling of a Tandem Line
275(1)
9.5.4 State Graph Modeling of a Conveyor-Driven Serial Line
275(4)
9.5.5 State Graph Modeling of Traffic Intersection Systems
279(4)
9.6 Simulation of Composite State Graph Models
283(16)
9.6.1 Framework of a State Graph Simulator
283(1)
9.6.2 Synchronization Manager
284(3)
9.6.3 Atomic Simulators
287(3)
9.6.4 Table Tennis Game Simulator
290(3)
9.6.5 State Graph Simulator for Reactive Systems
293(2)
9.6.6 SGS®
295(1)
Appendix 9A:DEVS
295(4)
9A.1 Definitions of DEVS
295(2)
9A.2 DEVS Simulators
297(2)
10 Advanced Topics in Activity-Based Modeling and Simulation
299(39)
10.1 Introduction
299(1)
10.2 Developing Your Own Activity Cycle Diagram Simulators
300(10)
10.2.1 Tocher's Three-Phase Process
300(2)
10.2.2 Activity Scanning Algorithm
302(2)
10.2.3 ACD Simulator
304(2)
10.2.4 P-ACD Simulator
306(3)
10.2.5 Collecting Statistics
309(1)
10.3 Modeling with Canceling Arc
310(3)
10.3.1 ACD Model of Single Server System with Reneging
311(1)
10.3.2 ACD Model of Resource Failure
312(1)
10.3.3 ACD Model of Time-Constrained Processing
313(1)
10.3.4 Execution of Canceling Arc
313(1)
10.4 Cycle Time Analysis of Work Cells via an Activity Cycle Diagram
313(9)
10.4.1 Cycle Time Analysis of Single-Armed Robot Work Cell
314(2)
10.4.2 Cycle Time Analysis of Single Hoist Plating Line
316(3)
10.4.3 Cycle Time Analysis of Dual-Armed Robot Cluster Tool
319(3)
10.5 Activity Cycle Diagram Modeling of a Flexible Manufacturing System
322(7)
10.5.1 ACD Modeling of Job Flows in FMS
323(1)
10.5.2 P-ACD Modeling of Job Routing in FMS
323(2)
10.5.3 P-ACD Modeling of AGV Dispatching Rules in FMS
325(2)
10.5.4 P-ACD Modeling of Refixture Operation and Heterogeneous FMS
327(2)
10.6 Formal Model Conversion
329(5)
10.6.1 Conversion of ACD Models to Event Graph (EG) Models
329(1)
10.6.2 Conversion of ACD Models to State Graph (SG) Models
330(1)
10.6.3 Examples of Formal Model Conversion
331(3)
Appendix 10A: Petri Nets
334(4)
10A.1 Definitions of Petri Nets
334(1)
10A.2 Petri-Net State and Execution
335(1)
10A.3 Extended Petri Nets and the ACD
336(1)
10A.4 Restricted Petri Nets
337(1)
10A.5 Modeling with Petri Nets
337(1)
11 Advanced Event Graph Modeling for Integrated Fab Simulation
338(33)
11.1 Introduction
338(1)
11.2 Flat Panel Display Fabrication System
339(4)
11.2.1 Overview of FPD Fab
339(1)
11.2.2 FPD Processing Equipment
340(2)
11.2.3 Material Handling System
342(1)
11.3 Production Simulation of a Flat Panel Display Fab
343(7)
11.3.1 Modeling of Uni-Inline Job Shop
343(2)
11.3.2 Modeling of Oven Type Job Shop
345(1)
11.3.3 Modeling of Heterogeneous Job Shop
346(1)
11.3.4 Object-Oriented Event Graph Simulator for Production Simulation
346(4)
11.4 Integrated Simulation of a Flat Panel Display Fab
350(12)
11.4.1 Modeling of Job Shop for Integrated Simulation
350(3)
11.4.2 Modeling of Conveyor Operation
353(2)
11.4.3 Modeling of the Interface between Conveyor and Inline Stocker
355(2)
11.4.4 Modeling of the Interface between Uni-inline Cells and Inline Stocker
357(1)
11.4.5 Modeling of the Interface between Oven and Inline Stocker
358(1)
11.4.6 Modeling of Inline Stocker Operation
358(3)
11.4.7 Integrated Fab Simulator
361(1)
11.5 Automated Material Handling Systems-Embedded Integrated Simulation of Flat Panel Display Fab
362(9)
11.5.1 Concept of AMHS-Embedded Fab Simulation
363(1)
11.5.2 Framework of AMHS-Embedded Fab Simulation System
364(2)
11.5.3 Simulator for AMHS-Embedded Integrated Fab Simulation
366(2)
11.5.4 IFS®
368(3)
12 Concepts and Applications of Parallel Simulation
371(24)
12.1 Introduction
371(1)
12.2 Parallel Simulation of Workflow Management System
372(6)
12.2.1 Enactment Service Mechanism of WfMS
372(1)
12.2.2 Framework of Parallel Simulation of WfMS
373(2)
12.2.3 State Graph Modeling of an Enactment Server and Sync Manager
375(2)
12.2.4 State Graph Modeling of Participant Simulators
377(1)
12.2.5 Implementation of a Workflow Simulator
377(1)
12.3 Overview of High-Level Architecture/Run-Time Infrastructure
378(5)
12.3.1 Basics of HLA/RTI
379(2)
12.3.2 HLA Federation Architecture
381(1)
12.3.3 Overview of Federation Execution
382(1)
12.4 Implementation of a Parallel Simulation with High-Level Architecture/Run-Time Infrastructure
383(12)
12.4.1 The Sushi Restaurant Federation
383(1)
12.4.2 Preparation of an FED File
384(2)
12.4.3 Preparation of the Federate Code (of the Production Federate)
386(5)
12.4.4 Executing the Restaurant Federation
391(4)
References 395(5)
Index 400
Byoung Kyu Choi is a professor in the Department of Industrial and Systems Engineering, KAIST, in Korea and a distinguished adjunct professor of Computer Science at KAU in Saudi Arabia. He received his PhD in Manufacturing Systems Engineering from Purdue University. Since 1983, he has taught and researched in the areas of CAD/CAM and system modeling at KAIST.

Donghun Kang received his PhD in Industrial and Systems Engineering from KAIST and has been working as a postdoctoral researcher at KAIST since February 2011.
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