Simulation Modeling and Analysis provides 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 172,000 copies in print and has been cited more than 18,500 times. This textbook can serve as the primary text for a variety of courses. It is used in leading industrial and systems engineering departments at Georgia Tech, University of Michigan, University of California at Berkeley, Stanford University, Purdue University, Texas A&M University, Columbia University, University of Washington, and Naval Postgraduate School.
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
New to this Edition
A CD containing the student version of the ExpertFit distribution-fitting software, will be included and will tie to the book.
The content on the treatment of the latest simulation software, including a common example in four of the leading products has been updated.
All software used in this textbook has been upgraded to FORTRAN and C.
20% of the problems in this edition, plus random-number generators, are new or have been updated.
This new edition contains statistical techniques for estimating the performance measures of a simulated system, both for terminating and steady-state simulations.
Also included in this edition are ranking-and-selection procedures for choosing the best system configuration, which allows the use of common random numbers for increased efficiency.
The discussion on how to use the method of common random numbers in practice, is now more detailed.
Also added, a self-contained discussion on classical design of experiments, with a particular emphasis on how to correctly implement these techniques in the context of simulation modeling.
Now included are several detailed examples on the use of simulation-based optimization.