Journal of the Operational Research Society

, Volume 62, Issue 1, pp 198–205

Robust parameter design optimization of simulation experiments using stochastic perturbation methods

  • A K Miranda
  • E Del Castillo
Theoretical Paper

DOI: 10.1057/jors.2009.163

Cite this article as:
Miranda, A. & Del Castillo, E. J Oper Res Soc (2011) 62: 198. doi:10.1057/jors.2009.163

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 

Copyright information

© Operational Research Society 2010

Authors and Affiliations

  • A K Miranda
    • 1
  • E Del Castillo
    • 1
  1. 1.The Pennsylvania State UniversityUSA

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