Simulation modeling and analysis
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Bibliographic Information
Simulation modeling and analysis
（McGraw-Hill series in industrial engineering and management science）
McGraw-Hill, 2007
4th ed., international ed
- : pbk
- Other Title
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Simulation modeling & analysis
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"International edition 2007"--T.p. verso
Includes bibliographical references and index
Description and Table of Contents
Description
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 100,000 copies in print.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 (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses.
*A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). 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 (part of Chaps. 1, 3, 5, 6, and 9).
Table of Contents
1. Basic Simulation Modeling2. Modeling Complex Systems3. Simulation Software4. Review of Basic Probability and Statistics5. Building Valid, Credible, and Appropriately Detailed Simulation Models6. Selecting Input Probability Distributions7. Random-Number Generators8. Generating Random Variates9. Output Data Analysis for a Single System10. Comparing Alternative System Configurations11. Variance-Reduction Techniques12. Experimental Design and Optimization13. Simulation of Manufacturing Systems
by "Nielsen BookData"
