Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints

Abstract

Metamodels are commonly used as fast surrogates for the objective function to facilitate the optimization of simulation models. Kriging (or the Gaussian process model) is a very popular metamodel form for deterministic and, recently, stochastic simulations. This article proposes a two-stage sequential framework for the optimization of stochastic simulations with heterogeneous variances under computing budget constraints. The proposed two-stage framework is based on the kriging model and incorporates optimal computing budget allocation techniques and the expected improvement function to drive and improve the estimation of the global optimum. Empirical results indicate that it is effective in obtaining optimal solutions and is more efficient than alternative metamodel-based techniques. The framework is also applied to a complex real ocean liner bunker fuel management problem with promising results.