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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.