Simulation Modeling and Analysis with ARENA

Höfundar: Altiok, Tayfur; Melamed, Benjamin (Útgáfa: 0)
Simulation Modeling and Analysis with ARENA

Kaup valmöguleikar

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.

· 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.

Nánar um bókina

Útgefandi
Elsevier S & T
ISBN
9780123705235
Print ISBN
9780123705235
Format
Page Fidelity (PDF)
Útgáfa
0
Höfundar
Altiok, Tayfur; Melamed, Benjamin
Tungumál
English
Útgefið
07/2010
Prent takmörkun á líftíma
10
Prent takmörkun
10
Afritunar takmörkun
10

Kaflar

  • Contents
  • Preface
  • Acknowledgments
  • 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 TES+ 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