Robust parameter design optimization of simulation experiments using stochastic perturbation methods

A K Miranda and E Del Castillo
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A K Miranda: The Pennsylvania State University
E Del Castillo: The Pennsylvania State University

Journal of the Operational Research Society, 2011, vol. 62, issue 1, 198-205

Abstract: Abstract Stochastic perturbation methods can be applied to problems for which either the objective function is represented analytically, or the objective function is the result of a simulation experiment. The Simultaneous Perturbation Stochastic Approximation (SPSA) method has the advantage over similar methods of requiring only two measurements at each iteration of the search. This feature makes SPSA attractive for robust parameter design (RPD) problems where some factors affect the variance of the response(s) of interest. In this paper, the feasibility of SPSA as a RPD optimizer is presented, first when the objective function is known, and then when the objective function is estimated by means of a discrete-event simulation.

Keywords: simulation optimization; noise factors; crossed arrays; non-homogeneous variance (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1057/jors.2009.163

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