Constrained optimization in expensive simulation: Novel approach
Abstract
This article presents a novel heuristic for constrained optimization of computationally expensive random simulation models. One output is selected as objective to be minimized, while other outputs must satisfy given threshold values. Moreover, the simulation inputs must be integer and satisfy linear or nonlinear constraints. The heuristic combines (i) sequentialized experimental designs to specify the simulation input combinations, (ii) Kriging (or Gaussian process or spatial correlation modeling) to analyze the global simulation input/output data resulting from these designs, and (iii) integer nonlinear programming to estimate the optimal solution from the Kriging metamodels. The heuristic is applied to an (s,S) inventory system and a call-center simulation, and compared with the popular commercial heuristic OptQuest embedded in the Arena versions 11 and 12. In these two applications the novel heuristic outperforms OptQuest in terms of number of simulated input combinations and quality of the estimated optimum.Download Info
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Bibliographic Info
Article provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 202 (2010)
Issue (Month): 1 (April)
Pages: 164-174
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Related research
Keywords: Simulation Global optimization Heuristics Kriging Bootstrap;Other versions of this item:
- Kleijnen, Jack P.C. & van Beers, Wim & Van Nieuwenhuyse, Inneke, 2010. "Constrained optimization in expensive simulation: novel approach," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/230053, Katholieke Universiteit Leuven.
References
References listed on IDEASPlease report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
- Driessen, L. & Brekelmans, R.C.M. & Gerichhausen, M. & Hamers, H.J.M. & Hertog, D. den, 2006. "Why Methods for Optimization Problems with Time-Consuming Function Evaluations and Integer Variables Should Use Global Approximation Models," Discussion Paper 2006-4, Tilburg University, Center for Economic Research.
- Kleijnen, J.P.C. & Beers, W.C.M. van, 2003. "Application-driven Sequential Designs for Simulation Experiments: Kriging Metamodeling," Discussion Paper 2003-33, Tilburg University, Center for Economic Research.
- Kleijnen, J.P.C., 2007.
"Kriging Metamodeling in Simulation: A Review,"
Discussion Paper
2007-13, Tilburg University, Center for Economic Research.
- Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
- Driessen, L. & Brekelmans, R.C.M. & Hamers, H.J.M. & Hertog, D. den, 2001. "On D-Optimality Based Trust Regions for Black-Box Optimization Problems," Discussion Paper 2001-69, Tilburg University, Center for Economic Research.
- Angun, M.E. & Gürkan, G. & Hertog, D. den & Kleijnen, J.P.C., 2002. "Response surface methodology revisited," Open Access publications from Tilburg University urn:nbn:nl:ui:12-91399, Tilburg University.
- Kleijnen, J.P.C., 2008. "Review of the book [Design and Analysis of Simulation Experiments]," Open Access publications from Tilburg University urn:nbn:nl:ui:12-4379049, Tilburg University.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Jack Kleijnen & Wim Beers & Inneke Nieuwenhuyse, 2012.
"Expected improvement in efficient global optimization through bootstrapped kriging,"
Journal of Global Optimization,
Springer, vol. 54(1), pages 59-73, September.
- Van Nieuwenhuyse, Inneke & Kleijnen, Jack & van Beers, Wim, 2010. "Expected improvement in efficient global optimization through bootstrapped kriging," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/278222, Katholieke Universiteit Leuven.
- Kleijnen, Jack & van Beers, Wim & Van Nieuwenhuyse, Inneke, 2012. "Expected improvement in efficient global optimization through bootstrapped kriging," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/310611, Katholieke Universiteit Leuven.
- Kabirian, Alireza & Ólafsson, Sigurdur, 2011. "Continuous optimization via simulation using Golden Region search," European Journal of Operational Research, Elsevier, vol. 208(1), pages 19-27, January.
- Arreola-Risa, Antonio & Giménez-García, Víctor M. & Martínez-Parra, José Luis, 2011. "Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis," European Journal of Operational Research, Elsevier, vol. 213(1), pages 107-118, August.
- Kleijnen, Jack P.C. & Mehdad, E., 2012. "Kriging in Multi-response Simulation, including a Monte Carlo Laboratory," Discussion Paper 2012-039, Tilburg University, Center for Economic Research.
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