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An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application

Authors:

Shih-Cheng Horng,

Shieh-Shing LinAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 36, Issue 5

Pages 9340 - 9349

Published: 01 July 2009 Publication History

Abstract

In this paper, we have proposed an ordinal optimization theory-based two-stage algorithm to solve for a good enough solution of the stochastic simulation optimization problem with huge input-variable space @Q. In the first stage, we construct a crude but effective model for the considered problem based on an artificial neural network. This crude model will then be used as a fitness function evaluation tool in a genetic algorithm to select N excellent settings from @Q. In the second stage, starting from the selected N excellent settings we proceed with the existing goal softening searching procedures to search for a good enough solution of the considered problem. We applied the proposed algorithm to the reduction of overkills and retests in a wafer probe testing process, which is formulated as a stochastic simulation optimization problem that consists of a huge input-variable space formed by the vector of threshold values in the testing process. The vector of good enough threshold values obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency. We have also justified the performance of the proposed algorithm in a wafer probe testing process based on the ordinal optimization theory.

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Cited By

Horng SYang F(2018)Optimal base-stock policy of the assemble-to-order systemsArtificial Life and Robotics10.1007/s10015-012-0013-917:1(47-52)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1007/s10015-012-0013-9
Horng SLin SYang F(2012)Evolutionary algorithm for stochastic job shop scheduling with random processing timeExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.05039:3(3603-3610)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.050
Horng S(2011)Ordinal optimization based approach to the optimal resource allocation of grid computing systemMathematical and Computer Modelling: An International Journal10.1016/j.mcm.2011.02.04254:1-2(519-530)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.mcm.2011.02.042

An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms

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Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 36, Issue 5

July, 2009

894 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2009.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2009

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Cited By

Horng SYang F(2018)Optimal base-stock policy of the assemble-to-order systemsArtificial Life and Robotics10.1007/s10015-012-0013-917:1(47-52)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1007/s10015-012-0013-9
Horng SLin SYang F(2012)Evolutionary algorithm for stochastic job shop scheduling with random processing timeExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.09.05039:3(3603-3610)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.eswa.2011.09.050
Horng S(2011)Ordinal optimization based approach to the optimal resource allocation of grid computing systemMathematical and Computer Modelling: An International Journal10.1016/j.mcm.2011.02.04254:1-2(519-530)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.mcm.2011.02.042

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