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Simulation Modeling and Analysis,9780073401324
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Simulation Modeling and Analysis

by
Edition:
5th
ISBN13:

9780073401324

ISBN10:
0073401323
Format:
Hardcover
Pub. Date:
1/22/2014
Publisher(s):
McGraw-Hill Science/Engineering/Math

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What version or edition is this?
This is the 5th edition with a publication date of 1/22/2014.
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Summary

Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self-study. The book is widely regarded as the “bible” of simulation and now has more than 155,000 copies in print and has been cited more than 14,000 times. The book can serve as the primary text for a variety of courses; for example:

  • A first course in simulation at the junior, senior, or beginning-graduate-student level in Engineering, Manufacturing, Business, or Computer Science. At the end of such a course, the students will be prepared to carry out complete and correct simulation studies, and to take Advanced Simulation courses.

  • A second course in simulation for graduate students in any of the above disciplines. After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research.

  • An introduction to simulation as part of a general course in Operations Research or Management Science.
  • Table of Contents

    1 Basic Simulation Modeling

    2 Modeling Complex Systems

    3 Simulation Software

    4 Review of Basic Probability and Statistics

    5 Building Valid, Credible, and Appropriately Detailed Simulation Models

    6 Selecting Input Probability Distributions

    7 Random-Number Generators

    8 Generating Random Variates

    9 Output Data Analysis for a Single System

    10 Comparing Alternative System Configurations

    11 Variance-Reduction Techniques

    12 Experimental Design, Sensitivity Analysis, and Optimization

    13 Simulation of Manufacturing Systems



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