期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 261, 期 1, 页码 279-301出版社
ELSEVIER
DOI: 10.1016/j.ejor.2017.01.035
关键词
Simulation; Stochastic Kriging; Heterogeneous noise; Ranking and selection; Optimization via simulation
资金
- Research Foundation Flanders (FWO) [G.0822.12]
- Hercules Foundation
- Flemish Government - department EWI
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. (C) 2017 Elsevier B.V. All rights reserved.
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