This unique book develops the application of experimental statistical designs and analysis to discrete-event simulation modeling. It takes a practical perspective and orients the reader with examples of the role of simulation in modeling a system. The stages and steps for applying simulation are discussed by focusing on the important role of statistics. Examples are given about how to design an experiment using techniques such as classical designs, group screening, polynomial decomposition, and Taguchi designs. Using the ...
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This unique book develops the application of experimental statistical designs and analysis to discrete-event simulation modeling. It takes a practical perspective and orients the reader with examples of the role of simulation in modeling a system. The stages and steps for applying simulation are discussed by focusing on the important role of statistics. Examples are given about how to design an experiment using techniques such as classical designs, group screening, polynomial decomposition, and Taguchi designs. Using the statistical techniques discussed, a sound simulation model can be built and adequately tested before implementation. The book also shows how simulation results can be generalized by discussing in full the growing emphasis on simulation metamodeling. Examples of this approach are presented to show that reliable and simple models could be easily obtained. Furthermore, such models are applied within a decision framework to optimize the system of interest. This expands the power of simulation from being purely descriptive of the system to being a prescriptive model. The reader is exposed to potential problems and how such problems may be harnessed. Although the book discusses statistical techniques, it is written so as to be comprehensible to anyone with a basic background in statistics. The book is a good resource for consultants and simulation practitioners; it can also be used as a textbook for classes in simulation.
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