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Stochastic Simulation Optimization for Discrete Event Systems: Perturbation Analysis, Ordinal Optimization and Beyond

 

 

Publishers

:

World Scientific Publishing Co. Pte. Ltd., Singapore

Authors

: 

Chun-Hung Chen, Qing-Shan Jia and Loo Hay Lee

Title

: 

Stochastic Simulation Optimization for Discrete Event Systems: Perturbation Analysis, Ordinal Optimization and Beyond

Year of Publication

: 

2013

Pages

: 

245

ISBN

: 

9789814513005

Reviewer

: 

Krishna B. Misra

Status

:

Review Complete

 

 

The book consists of 11 chapters arranged in two parts followed by Preface and three Forewords arranged under two parts as follows:

 

 

 

 

 

 

Stochastic Simulation is a very powerful tool for analyzing complex modern systems, which may involve 9789814513005several discrete events and the system optimization require a formulation consisting of several hundred decision variations and constraints. With the advent of cluster and cloud computing, it is now possible to attempt stochastic simulation optimization for the complex discrete event systems. Nevertheless computational efficiency is still an elusive task to achieve firstly due to a large number of alternative designs that need to be simulated in the optimization process; secondly due to large number of simulation runs are required for each alternative design to obtain a meaningful statistical estimate. Very often, a system analyst has to compromise on factors such as simulation accuracy , modelling accuracy or the optimality of selected design .Although there have been several schemes proposed to address the problem of efficiency but it appears that the approach of perturbation analysis (PA) and ordinal optimization (OO) as enunciated by Professor Y.-C. Ho in 1979 originally and later on developed by him and his and his associates through 1980s and early 1990s, provides a power technique to address this problem.   The basic idea of perturbation analysis is to reconstruct a perturbed sample from the nominal one with slight changes in parameters.

The infinitesimal perturbation analysis approach provides a very efficient way of estimating derivatives in discrete events optimization. The first part of the book deals with PA and IPA, whereas the Part II includes a review and several new directions in which research in OO is progressing. The on sic idea of OO is to compare two designs ordinal basis (X is better than Y) rather than carry out cardinal comparison (How much X is better than Y).  The book therefore provides a good account of these two approaches which provide powerful techniques to solve a wide range of problems in as wide areas of application as telecommunication transportation, supply chain and manufacturing, health care and finance systems.  Besides being of academic interests of graduate students and researchers, from the area of probability and statistics, computer science etc., the book will provide in depth information for use to industrial and systems engineers.

 

 
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