Comparison of Kriging-based methods for simulation optimization with heterogeneous noise

Hamed Jalali, Inneke Van Nieuwenhuyse and Victor Picheny

No 519220, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: Many discrete simulation optimization techniques are unsuitable when the number of feasible solutions is large, or when the simulations are time-consuming. For problems with low dimensionality, Kriging-based algorithms may be used: in recent years, several algorithms have been proposed which extend the traditional Kriging-based methods (assuming deterministic outputs) to problems with noise. Our objective in this paper is to compare the relative performance of a number of these algorithms on a set of well-known test functions, assuming different patterns of heterogeneous noise. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based methods to solve engineering and/or business problems, and may be useful in the development of future Kriging-based algorithms.

Keywords: Simulation; Stochastic Kriging; Heterogeneous noise; Ranking and Selection; Discrete optimization via simulation (search for similar items in EconPapers)
Date: 2015-12
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Published in FEB Research Report KBI_1529

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