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

• DOI: 10.1016/j.ejor.2017.01.035
• Publication Date: 2017-01-27T02:31:44Z
• Authors: Hamed Jalali; Inneke Van Nieuwe...; Victor Picheny
• Keywords: 2. Zero hunger; 0211 other engineering and technologies; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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.