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Simulation modeling and analysis / Averill M. Law, W. David Kelton.

By: Contributor(s): Material type: TextTextSeries: McGraw-Hill series in industrial engineering and management sciencePublication details: New York : McGraw-Hill, c1982.Description: xiv, 400 p. : ill. ; 24 cmISBN:
  • 0070366969
Subject(s): DDC classification:
  • 001.434   19
Contents:
1. Basic simulation modeling -- 2. Modeling complex systems -- 3. Simulation langauges -- 4. Review of basic probability and statistics -- 5. Selecting input probability distributions -- 6. Random-number generators -- 7. Generating random variables -- 8. Output data analysis for a single system -- 9. Statistical techniques for comparing alternative systems -- 10. Validation of simulation models -- 11. Variance-reduction techniques -- 12. Experimental design and optimization.
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 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.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Books Books Main library General Stacks 001.434 / LA.S 1982 (Browse shelf(Opens below)) 1 Available 001211

Includes bibliographies and index.

1. Basic simulation modeling -- 2. Modeling complex systems -- 3. Simulation langauges -- 4. Review of basic probability and statistics -- 5. Selecting input probability distributions -- 6. Random-number generators -- 7. Generating random variables -- 8. Output data analysis for a single system -- 9. Statistical techniques for comparing alternative systems -- 10. Validation of simulation models -- 11. Variance-reduction techniques -- 12. Experimental design and optimization.

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.

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