Abstract
Stochastic kriging is a popular metamodeling technique to approximate computationally expensive simulation models. However, it typically treats the simulation model as a black box in practice and often fails to capture the highly nonlinear response surfaces that arise from queueing simulations. We propose a simple, effective approach to improve the performance of stochastic kriging by incorporating stylized queueing models which contain useful information about the shape of the response surface. We provide several statistical tools to measure usefulness of the incorporated stylized models. We show that even a relatively crude stylized model can improve the prediction accuracy of stochastic kriging substantially.
Xiaowei Zhang
Associate Professor
My research research focuses on methodological advances in stochastic simulation and optimization, decision analytics, and reinforcement learning, with applications in service operations management, financial technology, and digital economy.