Simulation Modeling and Analysis with ARENA - 1st Edition - ISBN: 9780123705235, 9780080548951

Simulation Modeling and Analysis with ARENA

1st Edition

Authors: Tayfur Altiok Benjamin Melamed
eBook ISBN: 9780080548951
Hardcover ISBN: 9780123705235
Imprint: Academic Press
Published Date: 22nd June 2007
Page Count: 456
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Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment.” It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings.

Key Features

· Introduces the concept of discrete event Monte Carlo simulation, the most commonly used methodology for modeling and analysis of complex systems · Covers essential workings of the popular animated simulation language, ARENA, including set-up, design parameters, input data, and output analysis, along with a wide variety of sample model applications from production lines to transportation systems · Reviews elements of statistics, probability, and stochastic processes relevant to simulation modeling

  • Ample end-of-chapter problems and full Solutions Manual
  • Includes CD with sample ARENA modeling programs


Junior and Senior undergraduate students taking required courses in “Simulation,” and related courses in “Simulation and Modeling,” and “Modeling and Analysis,” often mandated for majors in Industrial and Mechanical engineering programs, as well as offered for students in other engineering disciplines, including civil, electrical, and chemical engineering, among others. Graduate students in business programs taking courses in industrial management, and related areas in quality control, modeling of complex systems.

Table of Contents

Chapter 1 Introduction to Simulation Modeling 1.1 Systems and Models 1.2 Analytical Versus Simulation Modeling 1.3 Simulation Modeling and Analysis 1.4 Simulation Worldviews 1.5 Model Building 1.6 Simulation Costs and Risks 1.7 Example: A Production Control Problem 1.8 Project Report Exercises Chapter 2 Discrete Event Simulation 2.1 Elements of Discrete Event Simulation 2.2 Examples of DES Models 2.2.1 Single Machine 2.2.2 Single Machine with Failures 2.2.3 Single Machine with an Inspection Station and Associated Inventory 2.3 Monte Carlo Sampling and Histories 2.3.1 Example: Work Station Subject to Failures and Inventory Control 2.4 DES Languages Exercises Chapter 3 Elements of Probability and Statistics 3.1 Elementary Probability Theory 3.1.1 Probability Spaces 3.1.2 Conditional Probabilities 3.1.3 Dependence and Independence 3.2 Random Variables 3.3 Distribution Functions 3.3.1 Probability Mass Functions 3.3.2 Cumulative Distribution Functions 3.3.3 Probability Density Functions 3.3.4 Joint Distributions 3.4 Expectations 3.5 Moments 3.6 Correlations 3.7 Common Discrete Distributions 3.7.1 Generic Discrete Distribution 3.7.2 Bernoulli Distribution 3.7.3 Binomial Distribution 3.7.4 Geometric Distribution 3.7.5 Poisson Distribution 3.8 Common Continuous Distributions 3.8.1 Uniform Distribution 3.8.2 Step Distribution 3.8.3 Triangular Distribution 3.8.4 Exponential Distribution 3.8.5 Normal Distribution 3.8.6 Lognormal Distribution 3.8.7 Gamma Distribution 3.8.8 Student´s t Distribution 3.8.9 F Distribution 3.8.10 Beta Distribution 3.8.11 Weibull Distribution 3.9 Stochastic Processes 3.9.1 Iid Processes 3.9.2 Poisson Processes 3.9.3 Regenerative (Renewal) Processes 3.9.4 Markov Processes 3.10 Estimation 3.11 Hypothesis Testing Exercises Chapter 4 Random Number and Variate Generation 4.1 Variate and Process Generation 4.2 Variate Generation Using the Inverse Transform Method 4.2.1 Generation of Uniform Variates 4.2.2 Generation of Exponential Variates 4.2.3 Generation of Discrete Variates 4.2.4 Generation of Step Variates from Histograms 4.3 Process Generation 4.3.1 Iid Process Generation 4.3.2 Non-Iid Process Generation Exercises Chapter 5 Arena Basics 5.1 Arena Home Screen 5.1.1 Menu Bar 5.1.2 Project Bar 5.1.3 Standard Toolbar 5.1.4 Draw and View Bars 5.1.5 Animate and Animate Transfer Bars 5.1.6 Run Interaction Bar 5.1.7 Integration Bar 5.1.8 Debug Bar 5.2 Example: A Simple Workstation 5.3 Arena Data Storage Objects 5.3.1 Variables 5.3.2 Expressions 5.3.3 Attributes 5.4 Arena Output Statistics Collection 5.4.1 Statistics Collection via the Statistic Module 5.4.2 Statistics Collection via the Record Module 5.5 Arena Simulation and Output Reports 5.6 Example: Two Processes in Series 5.7 Example: A Hospital Emergency Room 5.7.1 Problem Statement 5.7.2 Arena Model 5.7.3 Emergency Room Segment 5.7.4 On-Call Doctor Segment 5.7.5 Statistics Collection 5.7.6 Simulation Output 5.8 Specifying Time-Dependent Parameters via a Schedule Exercises Chapter 6 Model Testing and Debugging Facilities 6.1 Facilities for Model Construction 6.2 Facilities for Model Checking 6.3 Facilities for Model Run Control 6.3.1 Run Modes 6.3.2 Mouse-Based Run Control 6.3.3 Keyboard-Based Run Control 6.4 Examples of Run Tracing 6.4.1 Example: Open-Ended Tracing 6.4.2 Example: Tracing Selected Blocks 6.4.3 Example: Tracing Selected Entities 6.5 Visualization and Animation 6.5.1 Animate Connectors Button 6.5.2 Animate Toolbar 6.5.3 Animate Transfer Toolbar 6.6 Arena Help Facilities 6.6.1 Help Menu 6.6.2 Help Button Exercises Chapter 7 Input Analysis 7.1 Data Collection 7.2 Data Analysis 7.3 Modeling Time Series Data 7.3.1 Method of Moments 7.3.2 Maximal Likelihood Estimation Method 7.4 Arena Input Analyzer 7.5 Goodness-of-Fit Tests for Distributions 7.5.1 Chi-Square Test 7.5.2 Kolmogorov-Smirnov (K-S) Test 7.6 Multimodal Distributions Exercises Chapter 8 Model Goodness: Verification and Validation 8.1 Model Verification via Inspection of Test Runs 8.1.1 Input Parameters and Output Statistics 8.1.2 Using a Debugger 8.1.3 Using Animation 8.1.4 Sanity Checks 8.2 Model Verification via Performance Analysis 8.2.1 Generic Workstation as a Queueing System 8.2.2 Queueing Processes and Parameters 8.2.3 Service Disciplines 8.2.4 Queueing Performance Measures 8.2.5 Regenerative Queueing Systems and Busy Cycles 8.2.6 Throughput 8.2.7 Little´s Formula 8.2.8 Steady-State Flow Conservation 8.2.9 PASTA Property 8.3 Examples of Model Verification 8.3.1 Model Verification in a Single Workstation 8.3.2 Model Verification in Tandem Workstations 8.4 Model Validation Exercises Chapter 9 Output Analysis 9.1 Terminating and Steady-State Simulation Models 9.1.1 Terminating Simulation Models 9.1.2 Steady-State Simulation Models 9.2 Statistics Collection from Replications 9.2.1 Statistics Collection Using Independent Replications 9.2.2 Statistics Collection Using Regeneration Points and Batch Means 9.3 Point Estimation 9.3.1 Point Estimation from Replications 9.3.2 Point Estimation in Arena 9.4 Confidence Interval Estimation 9.4.1 Confidence Intervals for Terminating Simulations 9.4.2 Confidence Intervals for Steady-State Simulations 9.4.3 Confidence Interval Estimation in Arena 9.5 Output Analysis via Standard Arena Output 9.5.1 Working Example: A Workstation with Two Types of Parts 9.5.2 Observation Collection 9.5.3 Output Summary 9.5.4 Statistics Summary: Multiple Replications 9.6 Output Analysis via the Arena Output Analyzer 9.6.1 Data Collection 9.6.2 Graphical Statistics 9.6.3 Batching Data for Independent Observations 9.6.4 Confidence Intervals for Means and Variances 9.6.5 Comparing Means and Variances 9.6.6 Point Estimates for Correlations 9.7 Parametric Analysis via the Arena Process Analyzer Exercises Chapter 10 Correlation Analysis 10.1 Correlation in Input Analysis 10.2 Correlation in Output Analysis 10.3 Autocorrelation Modeling with TES Processes 10.4 Introduction to TES Modeling 10.4.1 Background TES Processes 10.4.2 Foreground TES Processes 10.4.3 Inversion of Distribution Functions 10.5 Generation of TES Sequences Generation of TESþ Sequences Generation of TES_ Sequences Combining TES Generation Algorithms 10.6 Example: Correlation Analysis in Manufacturing Systems Exercises Chapter 11 Modeling Production Lines 11.1 Production Lines 11.2 Models of Production Lines 11.3 Example: A Packaging Line 11.3.1 An Arena Model 11.3.2 Manufacturing Process Modules 11.3.3 Model Blocking Using the Hold Module 11.3.4 Resources and Queues 11.3.5 Statistics Collection 11.3.6 Simulation Output Reports 11.4 Understanding System Behavior and Model Verification 11.5 Modeling Production Lines via Indexed Queues and Resources 11.6 An Alternative Method of Modeling Blocking 11.7 Modeling Machine Failures 11.8 Estimating Distributions of Sojourn Times 11.9 Batch Processing 11.10 Assembly Operations 11.11 Model Verification for Production Lines Exercises Chapter 12 Modeling Supply Chain Systems 12.1 Example: A Production/Inventory System 12.1.1 Problem Statement 12.1.2 Arena Model 12.1.3 Inventory Management Segment 12.1.4 Demand Management Segment 12.1.5 Statistics Collection 12.1.6 Simulation Output 12.1.7 Experimentation and Analysis 12.2 Example: A Multiproduct Production/Inventory System 12.2.1 Problem Statement 12.2.2 Arena Model 12.2.3 Inventory Management Segment 12.2.4 Demand Management Segment 12.2.5 Model Input Parameters and Statistics 12.2.6 Simulation Results 12.3 Example: A Multiechelon Supply Chain 12.3.1 Problem Statement 12.3.2 Arena Model 12.3.3 Inventory Management Segment for Retailer 12.3.4 Inventory Management Segment for Distribution Center 12.3.5 Inventory Management Segment for Output Buffer 12.3.6 Production/Inventory Management Segment for Input Buffer 12.3.7 Inventory Management Segment for Supplier 12.3.8 Statistics Collection 12.3.9 Simulation Results Exercises Chapter 13 Modeling Transportation Systems 13.1 Advanced Transfer Template Panel 13.2 Animate Transfer Toolbar 13.3 Example: A Bulk-Material Port 13.3.1 Ship Arrivals 13.3.2 Tug Boat Operations 13.3.3 Coal-Loading Operations 13.3.4 Tidal Window Modulation 13.3.5 Simulation Results 13.4 Example: A Toll Plaza 13.4.1 Arrivals Generation 13.4.2 Dispatching Cars to Tollbooths 13.4.3 Serving Cars at Tollbooths 13.4.4 Simulation Results for the Toll Plaza Model 13.5 Example: A Gear Manufacturing Job Shop 13.5.1 Gear Job Arrivals 13.5.2 Gear Transportation 13.5.3 Gear Processing 13.5.4 Simulation Results for the Gear Manufacturing Job Shop Model 13.6 Example: Sets Version of the Gear Manufacturing Job Shop Model Exercises Chapter 14 Modeling Computer Information Systems 14.1 Client/Server System Architectures 14.1.1 Message-Based Communications 14.1.2 Client Hosts 14.1.3 Server Hosts 14.2 Communications Networks 14.3 Two-Tier Client/Server Example: A Human Resources System 14.3.1 Client Nodes Segment 14.3.2 Communications Network Segment 14.3.3 Server Node Segment 14.3.4 Simulation Results 14.4 Three-Tier Client/Server Example: An Online Bookseller System 14.4.1 Request Arrivals and Transmission Network Segment 14.4.2 Transmission Network Segment 14.4.3 Server Nodes Segment 14.4.4 Simulation Results Exercises Appendix A Frequently Used Arena Constructs A.1 Frequently Used Arena Built-in Variables A.1.1 Entity-Related Attributes and Variables A.1.2 Simulation Time Variables A.1.3 Expressions A.1.4 General-Purpose Global Variables A.1.5 Queue Variables A.1.6 Resource Variables A.1.7 Statistics Collection Variables A.1.8 Transporter Variables A.1.9 Miscellaneous Variables and Functions A.2 Frequently Used Arena Modules A.2.1 Access Module (Advanced Transfer) A.2.2 Assign Module (Basic Process) A.2.3 Batch Module (Basic Process) A.2.4 Create Module (Basic Process) A.2.5 Decide Module (Basic Process) A.2.6 Delay Module (Advanced Process) A.2.7 Dispose Module (Basic Process) A.2.8 Dropoff Module (Advanced Process) A.2.9 Free Module (Advanced Transfer) A.2.10 Halt Module (Advanced Transfer) A.2.11 Hold Module (Advanced Process) A.2.12 Match Module (Advanced Process) A.2.13 PickStation Module (Advanced Transfer) A.2.14 Pickup Module (Advanced Process) A.2.15 Process Module (Basic Process) A.2.16 ReadWrite Module (Advanced Process) A.2.17 Record Module (Basic Process) A.2.18 Release Module (Advanced Process) A.2.19 Remove Module (Advanced Process) A.2.20 Request Module (Advanced Transfer) A.2.21 Route Module (Advanced Transfer) A.2.22 Search Module (Advanced Process) A.2.23 Seize Module (Advanced Process) A.2.24 Separate Module (Basic Process) A.2.25 Signal Module (Advanced Process) A.2.26 Station Module (Advanced Transfer) A.2.27 Store Module (Advanced Process) A.2.28 Transport Module (Advanced Transfer) A.2.29 Unstore Module (Advanced Process) A.2.30 VBA Block (Blocks) Appendix B VBA in Arena B.1 Arena’s Object Model B.2 Arena’s Type Library B.2.1 Resolving Object Name Ambiguities B.2.2 Obtaining Access to the Application Object B.3 Arena VBA Events B.4 Example: Using VBA in Arena B.4.1 Changing Inventory Parameters Just Before a Simulation Run B.4.2 Changing Inventory Parameters during a Simulation Run B.4.3 Changing Customer Arrival Distributions Just before a Simulation Run B.4.3 Writing Arena Data to Excel via VBA Code B.4.4 Reading Arena Data from Excel via VBA Code References Index


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© Academic Press 2007
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About the Author

Tayfur Altiok

Affiliations and Expertise

Professor Department of Industrial and Systems Engineering, Rutgers University, New Jersey

Benjamin Melamed

Affiliations and Expertise

Professor, Department of Management Science and Information Systems, Rutgers Business School, Rutgers University, New Jersey

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