Machine Learning-Assisted Stochastic Kriging Metamodel for Offline Simulation Online Application

For many real-time decision problems in complex stochastic systems, a decision maker observes the system status online in real time and needs to immediately evaluate the performance of alternative decision choices based on the observed system status. Simulation metamodels can effectively support these needs by constructing a fast-to-evaluate mapping from the system status and decision to system performance using offline-generated simulation samples. However, when the system status involves high-dimensional information and a large number of simulated samples, classical simulation metamodeling approaches, such as stochastic kriging, may face challenges in terms of model specification, computational complexities, and computer storage demands. To address these challenges, we propose using machine learning models to assist stochastic kriging in building metamodels offline, which then can support online applications. The machine learning models can capture the potential nonlinear dependence of the stochastic kriging parameters on the high-dimensional system status. We analyze standard properties, such as mean squared errors and uncertainty quantification, for the proposed machine learning-assisted metamodel, and we show its consistency and asymptotic validity. We demonstrate the comparative advantage of our approach through numerical experiments.

History: Accepted by Bruno Tuffin, Area Editor for Simulation.

Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0130) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0130). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.