Constrained Optimization in Simulation: A Novel Approach

Jack P.C. Kleijnen, W.C.M. van Beers and . van Nieuwenhuyse
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. van Nieuwenhuyse: Tilburg University, Center for Economic Research

No 2008-95, Discussion Paper from Tilburg University, Center for Economic Research

Abstract: This paper presents a novel heuristic for constrained optimization of random computer simulation models, in which one of the simulation outputs is selected as the objective to be minimized while the other outputs need to satisfy prespeci¯ed target values. Besides the simulation outputs, the simulation inputs must meet prespeci¯ed constraints including the constraint that the inputs be integer. The proposed heuristic combines (i) experimental design to specify the simulation input combinations, (ii) Kriging (also called spatial correlation mod- eling) to analyze the global simulation input/output data that result from this experimental design, and (iii) integer nonlinear programming to estimate the optimal solution from the Krig- ing metamodels. The heuristic is applied to an (s, S) inventory system and a realistic call-center simulation model, and compared with the popular commercial heuristic OptQuest embedded in the ARENA versions 11 and 12. These two applications show that the novel heuristic outper- forms OptQuest in terms of search speed (it moves faster towards high-quality solutions) and consistency of the solution quality.

JEL-codes: C0 C1 C9 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba and nep-cmp
Date: 2008
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