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Response surface methodology with stochastic constraints for expensive simulation

Author

Listed:
  • E Angün

    (Galatasaray University)

  • J Kleijnen

    (Tilburg University)

  • D den Hertog

    (Tilburg University)

  • G Gürkan

    (Tilburg University)

Abstract

This article investigates simulation-based optimization problems with a stochastic objective function, stochastic output constraints, and deterministic input constraints. More specifically, it generalizes classic response surface methodology (RSM) to account for these constraints. This Generalized RSM—abbreviated to GRSM—generalizes the estimated steepest descent—used in classic RSM—applying ideas from interior point methods, especially affine scaling. This new search direction is scale independent, which is important for practitioners because it avoids some numerical complications and problems commonly encountered. Furthermore, the article derives a heuristic that uses this search direction iteratively. This heuristic is intended for problems in which simulation runs are expensive, so that the search needs to reach a neighbourhood of the true optimum quickly. The new heuristic is compared with OptQuest, which is the most popular heuristic available with several simulation software packages. Numerical illustrations give encouraging results.

Suggested Citation

  • E Angün & J Kleijnen & D den Hertog & G Gürkan, 2009. "Response surface methodology with stochastic constraints for expensive simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 735-746, June.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:6:d:10.1057_palgrave.jors.2602614
    DOI: 10.1057/palgrave.jors.2602614
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    References listed on IDEAS

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    Cited by:

    1. Kuo-Hao Chang & L. Jeff Hong & Hong Wan, 2013. "Stochastic Trust-Region Response-Surface Method (STRONG)---A New Response-Surface Framework for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 230-243, May.
    2. Antonio Del Prete & Rodolfo Franchi & Stefania Cacace & Quirico Semeraro, 2020. "Optimization of cutting conditions using an evolutive online procedure," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 481-499, February.
    3. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    4. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.

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