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

Hamed Jalali, Inneke Van Nieuwenhuyse and Victor Picheny

European Journal of Operational Research, 2017, vol. 261, issue 1, 279-301

Abstract: In this article we investigate the unconstrained optimization (minimization) of the performance of a system that is modeled through a discrete-event simulation. In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization algorithms (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 analytical test functions, assuming different patterns of heterogeneous noise. We also apply the algorithms to a popular inventory test problem. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based algorithms to solve engineering and/or business problems, and may be useful in the development of future algorithms.

Keywords: Simulation; Stochastic Kriging; Heterogeneous noise; Ranking and selection; Optimization via simulation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:261:y:2017:i:1:p:279-301

DOI: 10.1016/j.ejor.2017.01.035

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