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Discrete-event system simulation / Jerry Banks ... [y otros].

Contributor(s): Banks, Jerry [autor] | Carson, John S [autor] | Nelson, Barry L [autor] | Nicol, David M [autor].
Series: Prentice-Hall International Series in Industrial and Systems Engineering.Publisher: Upper Saddle River, N.J. : Pearson Prentice Hall, ©2005Edition: 4th. ed.Description: xvi, 608 páginas : ilustraciones, tablas ; 24 cm.Content type: texto Media type: no mediado Carrier type: volumenISBN: 0131446797.Subject(s): Ingenieria industrial | Metodos de simulacionDDC classification: 003.83
Contents:
Introduction to Simulation. -- Simulation examples. -- General principles. -- Simulation software. -- Mathematical and statistical models. -- Queueing models. -- Random number generation. -- Random-variate generation. -- Input modeling. -- Verification and validation of simulation models. -- Output Analysis for a Single model. -- Comparasion and evaluation of alternative system designs. -- Simulation of manufacturing and material-Handling systems. -- Simulation of computer systems. -- Simulation of computer Networks.
Summary: Discrete Event System Simulation is ideal for junior- and senior-level simulation courses in engineering, business, or computer science. It is also a useful reference for professionals in operations research, management science, industrial engineering, and information science. While most books on simulation focus on particular software tools, Discrete Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks. Students and instructors will find a variety of resources at the associated website, www.bcnn.net/, including simulation source code for download, additional exercises and solutions, web links and errata.
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Item type Current location Collection Call number Vol info Copy number Status Date due Barcode Item holds
Book Book B. Posgrados
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Colección general 003.83 D611 (Browse shelf) 4a ed. 2005 1 Available 0000030258
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Enhanced descriptions from Syndetics:

For Junior & Senior level simulation courses in engineering, business, or computer science.

This text provides a basic treatment of discrete-event simulation, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks.

Students and instructors will find a variety of resources at the associated website, www.bcnn.net , including simulation source code for download, additional exercises and solutions, web links and errata.

Include index., Appendix

Introduction to Simulation. -- Simulation examples. -- General principles. -- Simulation software. -- Mathematical and statistical models. -- Queueing models. -- Random number generation. -- Random-variate generation. -- Input modeling. -- Verification and validation of simulation models. -- Output Analysis for a Single model. -- Comparasion and evaluation of alternative system designs. -- Simulation of manufacturing and material-Handling systems. -- Simulation of computer systems. -- Simulation of computer Networks.

Discrete Event System Simulation is ideal for junior- and senior-level simulation courses in engineering, business, or computer science. It is also a useful reference for professionals in operations research, management science, industrial engineering, and information science. While most books on simulation focus on particular software tools, Discrete Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks. Students and instructors will find a variety of resources at the associated website, www.bcnn.net/, including simulation source code for download, additional exercises and solutions, web links and errata.

Table of contents provided by Syndetics

  • I Introduction To Discrete-Event System Simulation
  • 1 Introduction to Simulation
  • When Simulation Is the Appropriate Tool
  • When Simulation Is Not Appropriate
  • Advantages and Disadvantages of Simulation
  • Areas of Application
  • Systems and System Environment
  • Components of a System
  • Discrete and Continuous Systems
  • Model of a System
  • Types of Models
  • Discrete-Event System Simulation
  • Steps in a Simulation Study
  • 2 Simulation Examples
  • Simulation of Queueing Systems
  • Simulation of Inventory Systems
  • Other Examples of Simulation
  • 3 General Principles
  • Concepts in Discrete-Event Simulation
  • List Processing
  • 4 Simulation Software
  • History of Simulation Software
  • Selection of Simulation Software
  • An Example Simulation
  • Simulation in C++
  • Simulation in GPSS
  • Simulation in CSIM
  • Simulation Packages
  • Experimentation and Statistical Analysis Tools
  • Trends in Simulation Software
  • II Mathematical And Statistical Models
  • 5 Statistical Models in Simulation
  • Review of Terminology and Concepts
  • Useful Statistical Models
  • Discrete Distributions
  • Continuous Distributions
  • Poisson Process
  • Empirical Distributions
  • 6 Queueing Models
  • Characteristics of Queueing Systems
  • Queueing Notation
  • Long-Run Measures of Performance of Queueing Systems
  • Steady-State Behavior of Infinite-Population Markovian Models
  • Steady-State Behavior of Finite-Population Models
  • Networks of Queues
  • III Random Numbers
  • 7 Random-Number Generation
  • Properties of Random Numbers
  • Generation of Pseudo-Random Numbers
  • Techniques for Generating Random Numbers
  • Tests for Random Numbers
  • 8 Random-Variate Generation
  • Inverse Transform Technique
  • Direct Transformation for the Normal and Lognormal Distributions
  • Convolution Method
  • Acceptance-Rejection Technique
  • IV Analysis Of Simulation Data
  • 9 Input Modeling
  • Data Collection
  • Identifying the Distribution with Data
  • Parameter Estimation
  • Goodness-of-Fit Tests
  • Selecting Input Models without Data
  • Multivariate and Time-Series Input Models
  • 10 Verification and Validation of Simulation Models
  • Model Building, Verification, and Validation
  • Verification of Simulation Models
  • Calibration and Validation of Models
  • 11 Output Analysis for a Single Model
  • Types of Simulations with Respect to Output Analysis
  • Stochastic Nature of Output Data
  • Measures of Performance and Their Estimation
  • Output Analysis for Terminating Simulations
  • Output Analysis for Steady-State Simulations
  • 12 Comparison and Evaluation of Alternative System Designs
  • Comparison of Two System Designs
  • Comparison of Several System Designs
  • Metamodeling
  • Optimization via Simulation
  • 13 Simulation of Manufacturing and Material Handling Systems
  • Manufacturing and Material Handling Simulations
  • Goals and Performance Measures
  • Issues in Manufacturing and Material Handling Simulations
  • Case Studies of the Simulation of Manufacturing and Material Handling Systems
  • 14 Simulation of Computer Systems
  • Introduction
  • Simulation Tools
  • Model Input
  • High-Level Computer-System Simulation
  • CPU Simulation
  • Memory Simulation
  • Appendix Tables
  • Random Digits
  • Random Normal Numbers
  • Cumulative Normal Distribution
  • Cumulative Poisson Distribution
  • Percentage Points of the Students
  • Distribution with
  • Degrees of Freedom
  • Percentage Points of the Chi-Square Distribution with v
  • Degrees of Freedom
  • Percentage Points of the F
  • Distribution with âÇ a = 0.05
  • Kolmogorov-Smirnov Critical Values
  • Maximum-Likelihood Estimates of the Gamma Distribution
  • Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size
  • Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size
  • Index

Author notes provided by Syndetics

Jerry Banks retired in 1999 as a professor in the School of Industrial and Systems Engineering, Georgia Institute of Technology, after which he worked as senior simulation technology advisor for Brooks Automation; he is currently an independent consultant. He is the author, coauthor, editor, or coeditor of eleven books, one set of proceedings, several chapters in texts, and numerous technical papers. He is the editor of the Handbook of Simulation, published in 1998 by John Wiley, which won the award for Excellence in Engineering Handbooks from the Professional Scholarly Publishing Division of the Association of American Publishers, Inc. He is also author or coauthor of Getting Started with AutoMod, Second Edition, Introduction to SIMAN V and CINEMA V, Getting Started with GPSSIHH, Second Edition, Forecasting and Management of Technology and Principles of Quality Control. He was a founding partner in the simulation-consulting firm Carson/Banks & Associates, Inc., which was purchased by AutoSimulations, Inc. (now part of Brooks Automation). He is a full member of many technical societies, among them the Institute of Industrial Engineers (IIE); he served eight years as that organization''s representative to the Board of the Winter Simulation Conference, including two years as board chair. He is the recipient of the INFORMS College on Simulation Distinguished Service Award for 1999 and was named a fellow of HE in 2002.

John S. Carson II is the consulting technical manager for the AutoMod Group at Brooks Automation. He has over 28 years experience in simulation in a wide range of application areas, including manufacturing, distribution, warehousing and material handling, transportation and rapid transit systems, port operations (container terminals and bulk handling), and health-care systems. Currently, he is involved in the design of next-generation simulation products and in the development of tools to speed up model development for semi-conductor manufacturing, distribution centers, container terminals and other areas of special interest. He co-founded and managed an independent simulation services company for 8 years, has been an independent simulation consultant, and has taught at the Georgia Institute of Technology, the University of Florida, and the University of Wisconsin-Madison.

Barry L. Nelson is the James N. and Margie M. Krebs Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University and is director of the Master of Engineering Management Program there. His research centers on the design and analysis of computer-simulation experiments on models of stochastic systems, concentrating on multivariate input modeling and output analysis and on optimization via simulation. He has published numerous papers and two books. He has served as the simulation area editor of Operations Research and as president of the INFORMS (then TIMS) College on Simulation, and he has held many positions for the annual Winter Simulation Conference, including program chair in 1997 and board member currently.

David M. Nicol is professor of electrical and computer engineering at the University of Illinois at Urbana-Champaign. He is a long-time contributor in the field of parallel and distributed discrete-event simulations, having written one of the early Ph.D. dissertations on the topic. He has also worked in parallel algorithms, algorithms for mapping workload in parallel architectures, performance analysis, and reliability modeling and analysis. His research contributions extend to 150 articles in leading computer-science journals and conferences. His research is driven largely by problems encountered in industry and government he has worked closely with researchers at NASA, IBM, AT&T, Bellcore, Motorola, and the Los Alamos, Sandia, and Oak Ridge National Laboratories. His current interests lie in modeling and simulation of very large systems, particularly communications and other infrastructure, with applications in evaluating system security. From 1997 to 2003 he was the editor-in-chief of the ACM Transactions on Modeling and Computer Simulation. Professor Nicol is a Fellow of the IEEE.

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