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A framework for response surface methodology for simulation optimization

Author

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  • Neddermeijer, H.G.
  • van Oortmarssen, G.J.
  • Piersma, N.
  • Dekker, R.

Abstract

We develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology. The framework is especially intended for simulation models where the calculation of the corresponding stochastic response function is very expensive or time-consuming. Response Surface Methodology is frequently used for the optimization of stochastic simulation models in a non-automated fashion. In scientific applications there is a clear need for a standardized algorithm based on Response Surface Methodology. In addition, an automated algorithm is less time-consuming, since there is no need to interfere in the optimization process. In our framework for automated optimization we describe all choices that have to be made in constructing such an algorithm.

Suggested Citation

  • Neddermeijer, H.G. & van Oortmarssen, G.J. & Piersma, N. & Dekker, R., 2000. "A framework for response surface methodology for simulation optimization," Econometric Institute Research Papers EI 2000-14/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1647
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    Citations

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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. Kleijnen, Jack P. C. & den Hertog, Dick & Angun, Ebru, 2004. "Response surface methodology's steepest ascent and step size revisited," European Journal of Operational Research, Elsevier, vol. 159(1), pages 121-131, November.
    3. Andrei A. Prudius & Sigrún Andradóttir, 2012. "Averaging frameworks for simulation optimization with applications to simulated annealing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 411-429, September.
    4. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Didier Maillard, 2019. "Computation of the corrected Cornish–Fisher expansion using the response surface methodology: application to VaR and CVaR," Annals of Operations Research, Springer, vol. 281(1), pages 423-453, October.
    5. Angun, M.E. & Gürkan, G. & den Hertog, D. & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Other publications TiSEM 32c35a04-3de9-4dee-a242-6, Tilburg University, School of Economics and Management.
    6. Angun, M.E., 2004. "Black box simulation optimization : Generalized response surface methodology," Other publications TiSEM 2548e953-54ce-44e2-8c5b-7, Tilburg University, School of Economics and Management.
    7. Yiduan Chen & Weinong Fu, 2015. "A Novel Hybrid-Flux Magnetic Gear and Its Performance Analysis Using the 3-D Finite Element Method," Energies, MDPI, vol. 8(5), pages 1-15, April.
    8. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    9. 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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    Keywords

    optimization; simulation;

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