Average Reviews:
(More customer reviews)Good coverage for scheduling-type or operations research simulation problems. Great coverage of non-uniform variable distributions, with useful (and hard to find elsewhere) code for generating them, as well as help in choosing which distribution is needed for a given problem. Taught me a lot.
Click Here to see more reviews about: Programming Discrete Simulations: Tools for Modeling the Real World
Discrete simulation uses statistical computer models to understand scheduling activities that are too complex for direct analysis. For example: How many lollipops must a grocer keep on the shelf? When does an intersection need a stoplight? Moshe Pollatschek offers a practical explanation of discrete simulation and provides a set of tools for writing discrete simulation programs. He adroitly bridges between explaining sound statistical principles and solving real-world problems with practical modeling. Part I establishes the statistical principles used for discrete event simulation. Part II describes all the routines of the Simulation Subroutine Set, a library of functions useful for building simulations. Part III provides fifteen example simulations ranging from simple telephone queue scheduling to a complex assembly task involving parts scheduling and component matching. Pollatschek thoroughly explores the interaction between programming methods and statistical analysis. For example, he clearly spells out the assumptions in techniques for variance reduction in terms of programming the model and does not bury them in obscure mathematical statements. He clearly demonstrates the interactions between programming considerations and mathematical algorithms. For example, he explains how to alter a random number generator to avoid domain problems. The Simulation Subroutine Set library and all example simulations are coded in C. Appendix b summarizes the parallel constructions for Fortran, Basic, and Pascal. The mathematical explanations are kept straightforward while still introducing sophisticated concepts such as sample path and bootstrapping in statistics, concepts often not treated in more advanced books. The mathematical understanding required of the reader is comparable to algebra. The main text uses nothing more involved than summation. A few optional analyses use simple integrals. This book breaks new ground in explaining the practical implications of the outlying tail of a distribution and the problems of finite maxima for the range of a variable.
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