Enhancing stochastic kriging for queueing simulation with stylized models

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 that contain useful information about the shape of the response surface. We provide several statistical tools to measure the usefulness of the incorporated stylized models. We show that even a relatively crude stylized model can substantially improve the prediction accuracy of stochastic kriging.

KEYWORDS:

• Stochastic kriging
• metamodel
• queueing simulation
• stylized queueing model

Acknowledgements

We thank the associate editor and two anonymous referees for their constructive comments that improved the article substantially.

Additional information

Funding

We gratefully acknowledge the support from the Hong Kong Research Grants Council under Project No. 9042362 and Project No. 16211417.

Notes on contributors

Haihui Shen

Haihui Shen is currently a Ph.D. student in the Department of Management Sciences in the College of Business at City University of Hong Kong. His research interests include stochastic simulation and optimization, ranking and selection, kriging metamodeling, and their applications.

L. Jeff Hong

L. Jeff Hong received his Ph.D. in industrial engineering and management science from Northwestern University and his B.S. in automotive engineering from Tsinghua University. He is currently the Endowed Chair Professor of Management Sciences in the College of Business at City University of Hong Kong. His research interests include stochastic simulation and optimization, financial engineering and risk management, and business analytics.

Xiaowei Zhang

Xiaowei Zhang received his Ph.D. in management science and engineering and M.S. in financial mathematics, both from Stanford University. He is currently an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. His research interests include stochastic simulation, statistical learning, and decision analytics.