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

摘要

In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization 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 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 methods to solve engineering and/or business problems, and may be useful in the development of future Kriging-based algorithms.