Kriging metamodeling in simulation: A review
AbstractThis article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas--contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs for sensitivity analysis and optimization. It ends with topics for future research.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 192 (2009)
Issue (Month): 3 (February)
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Kriging Metamodel Response surface Interpolation Optimization Design;
Other versions of this item:
- C0 - Mathematical and Quantitative Methods - - General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
Please 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. & van Beers, Wim C. M., 2005. "Robustness of Kriging when interpolating in random simulation with heterogeneous variances: Some experiments," European Journal of Operational Research, Elsevier, vol. 165(3), pages 826-834, September.
- Didier Rullière & Alaeddine Faleh & Frédéric Planchet & Wassim Youssef, 2013. "Exploring or reducing noise? A global optimization algorithm in the presence of noise," Post-Print hal-00759677, HAL.
- repec:hal:wpaper:hal-00759677 is not listed on IDEAS
- Kleijnen, Jack P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2010.
"Constrained optimization in simulation: A novel approach,"
Open Access publications from Tilburg University
urn:nbn:nl:ui:12-3583585, Tilburg University.
- Kleijnen, J.P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2008. "Constrained Optimization in Simulation: A Novel Approach," Discussion Paper 2008-95, Tilburg University, Center for Economic Research.
- Kleijnen, Jack P.C., 2013. "Simulation-Optimization via Kriging and Bootstrapping: A Survey (Revision of CentER DP 2011-064)," Discussion Paper 2013-064, Tilburg University, Center for Economic Research.
- Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
- 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.
- Bettonvil, B.W.M. & Castillo, E. del & Kleijnen, J.P.C., 2007. "Statistical Testing of Optimality Conditions in Multiresponse Simulation-based Optimization (Revision of 2005-81)," Discussion Paper 2007-45, Tilburg University, Center for Economic Research.
- Strang, Kenneth David, 2012. "Importance of verifying queue model assumptions before planning with simulation software," European Journal of Operational Research, Elsevier, vol. 218(2), pages 493-504.
- Rommel Regis & Christine Shoemaker, 2013. "A quasi-multistart framework for global optimization of expensive functions using response surface models," Journal of Global Optimization, Springer, vol. 56(4), pages 1719-1753, August.
- Mehdad, E. & Kleijnen, Jack P.C., 2013. "Bootstrapping and Conditional Simulation in Kriging: Better Confidence Intervals and Optimization," Discussion Paper 2013-038, Tilburg University, Center for Economic Research.
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