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Robust parameter design optimization of simulation experiments using stochastic perturbation methods

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  • A K Miranda

    (The Pennsylvania State University)

  • E Del Castillo

    (The Pennsylvania State University)

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.

Suggested Citation

  • A K Miranda & E Del Castillo, 2011. "Robust parameter design optimization of simulation experiments using stochastic perturbation methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 198-205, January.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:1:d:10.1057_jors.2009.163
    DOI: 10.1057/jors.2009.163
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    References listed on IDEAS

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    1. J S Yeomans, 2002. "Automatic generation of efficient policy alternatives via simulation-optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1256-1267, November.
    2. Y-C Ho & C G Cassandras & C-H Chen & L Dai, 2000. "Ordinal optimisation and simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(4), pages 490-500, April.
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    Cited by:

    1. Yanikoglu, I. & den Hertog, D. & Kleijnen, Jack P.C., 2013. "Adjustable Robust Parameter Design with Unknown Distributions," Other publications TiSEM 47fec228-1ffe-4803-8e97-5, Tilburg University, School of Economics and Management.
    2. Gabriella Dellino & Jack P. C. Kleijnen & Carlo Meloni, 2012. "Robust Optimization in Simulation: Taguchi and Krige Combined," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 471-484, August.

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