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Understanding Computer Simulation[RareAbyss]

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Description

 							
  
  
  This book describes
computer simulation concepts then provides basic details about using discrete-event computer simulation for decision
making. Input data collection and analysis, model construction, project mechanics, output analysis, verification,
validation, reporting, logic transfer, and robust experimental design are all covered in detail. Example models are
provided and illustrated using the GPSS simulation language, spreadsheet tools, and other products available on the Web.
Statistical analysis is covered, with considerations for using simulation in business and project settings.
  
  Content
  
  1.	Introduction to Computer Simulation	
  1.1	Simulation Defined	
 
1.1.1	Computer Simulation’s Basic Nature	
  1.1.2	Computer Simulation Uses	
  1.1.3 General Benefits of
Computer Simulation	
  1.1.4 General Limitations of Computer Simulation	
  1.2	Different Types of
Simulation	
  1.2.1 Continuous Simulation	
  1.2.2 Monte Carlo Simulation	
  1.2.3 Discrete Event
Computer Simulation	
  1.2.4 Agent-Based Modeling	
  1.3	Brief History of Simulation	
 
1.4	Bibliography
  
  2.	Simulation Languages	
  2.1	Simulation Language Features	
  2.1.1
Comparison to Traditional Languages	
  2.1.2 Simulation Languages	
  2.1.3 How Simulation Languages Work	
  2.2 Simulators and Integrated Simulation Environments	
  2.2.1 Simulators	
  2.2.2 Advantages of
Simulators	
  2.2.3 Simulators vs. Simulation Languages	
  2.2.4 Integrated Environments	
  2.3 Hardware
Requirements for Simulation	
  2.4 Animation	
  2.4.1 Using Animation in The Proper Context	
  2.4.2
Misuse of Animation	
  2.4.3 Animation Attributes	
  2.4.4 Example Animation Software Packages	
  2.5
Bibliography
  
  3.	Applications of Simulation	
  3.1 Why Use Simulation	
  3.2 Simulation
as a Design Tool	
  3.2.1 Types of Simulation	
  3.2.2 Operational Changes and Follow-up	
  3.3
Estimation of Simulation Time	
  3.4 Methodology for Manufacturing Simulations	
  3.5 Forcing Completion of
Design with Simulation	
  3.6 The Simulation Decision	
  3.7 Make It Work Vs. Does It Work

	
  3.8 Optimizing and Developing Solutions	
  3.9 Genetic Algorithms	
  3.10 Ethics in Simulation	
 
3.11 Bibliography
  
  4.	Starting a Simulation the Right Way	
  4.1 Intelligence	
  4.1.1
Problem Definition	
  4.1.2 General Feasibility	
  4.2 Managerial Phase	
  4.3 Developmental Phase	

 4.3.1 Model Scope	
  4.3.2 Modeling Views	
  4.3.3 Concept Model	
  4.3.4 Concept Modeling with Simple
Spreadsheets	
  4.3.5 Concept Modeling with Advanced Spreadsheets	
  4.3.6 Model Inputs	
  4.3.7
Statistical Methods and Input Data	
  4.3.8 Pearson’s Chi-Square Test	
  4.3.9 Example Chi-Square
Goodness-of-Fit	
  4.3.10 Kolmogorov-Smirnov Test	
  4.3.11 Anderson Darling Test	
  4.3.12 Example Using
Data Fit Software	
  4.4 Human Component Considerations	
  4.4.1 Simplementation Cases	
  4.4.2
Persims	
  4.5 Bibliography
  
  5.	Simulation Quality and Development	
  5.1 Quality
Assurance Phase	
  5.1.1 Validation	
  5.1.2 Simulation Input Data Validation	
  5.1.3 Validation of
Simulation Outputs	
  5.1.4 Summary of Validation	110
  5.2 Selection of a Language or Tool	
  5.3 Model
Construction	
  5.4 Verification	
  5.5 Bibliography
  
  6. Developing a
Simulation-Implementation	
  6.1 Experimental Design	
  6.1.1 Random and Pseudo-Random Number Streams	
  6.1.2 Special Concerns for Terminating Simulations	
  6.1.3 Special Concerns for Steady State Simulations	
  6.2 Production Runs	
  6.3 Output Analysis	
  6.3.1 Statistical Output Analysis of a Single Model
 
6.3.2 Statistical Output Analysis Used to Compare Models
  6.4 Output Reporting	
  6.4.1 Simulation Report	
  6.4.2 Quick Reports	
  6.4.3 Logic Transfer	
  6.5 Post Processing Output	
  6.5.1 MS-Office
Excel	
  6.5.2 SAS	
  6.5.3 Custom Generated Animation
  6.6. Operations, Maintenance and Archival
Phase
  6.7 Bibliography
  
  7.	Case Study: DePorres Tours	
  7.1 Intelligence Phase	
 
7.1.1 Problem Definition	
  7.1.2 General Feasibility	
  7.2 Managerial Phase	
  7.3 Developmental
Phase	
  7.3.1 Environment and Boundary	
  7.3.2 Model Scaling and Scope	
  7.3.3 Modeling Views	
 
7.3.4 Concept Model	
  7.3.5 Model Inputs	
  7.3.6 Simulation Input Data Validation	
  7.3.7 Selection of
a Language or Tool	
  7.3.8 Model Construction	
  7.4 Quality Phase	
  7.5 Implementation	
  7.5.1
Experimental Design	
  7.5.2 Statistical Output Analysis of a Single Model	
  7.5.3 Additional Scenario	

 7.6. Operations, Maintenance and Archival Phase	
  7.7 Bibliography	
  7.8 Appendix: GPSS World Source Code
Listing (Two Bus Model) 						

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