Simulation Modeling and Analysis by Averill M LawSimulation Modeling and Analysis by Averill M Law

Simulation Modeling and Analysis

byAverill M Law

Hardcover | January 22, 2014

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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.
  • Title:Simulation Modeling and AnalysisFormat:HardcoverDimensions:9.5 × 6.8 × 1.62 inPublished:January 22, 2014Publisher:McGraw-Hill EducationLanguage:English

    The following ISBNs are associated with this title:

    ISBN - 10:0073401323

    ISBN - 13:9780073401324

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    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