Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/30486
Title: Constrained optimization in expensive simulation: Novel approach
Authors: Kleijnen, JPC
van Beers, W
VAN NIEUWENHUYSE, Inneke 
Issue Date: 2010
Publisher: ELSEVIER
Source: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 202 (1) , p. 164 -174
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. (C) 2009 Elsevier B.V. All rights reserved.
Keywords: Simulation;Global optimization;Heuristics;Kriging;Bootstrap
Document URI: http://hdl.handle.net/1942/30486
ISSN: 0377-2217
e-ISSN: 1872-6860
DOI: 10.1016/j.ejor.2009.05.002
ISI #: WOS:000271700800020
Rights: 2009 Elsevier B.V. All rights reserved
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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