Gaussian process based optimization algorithms with input uncertainty

Abstract

Metamodels as cheap approximation models for expensive to evaluate functions have been commonly used in simulation optimization problems. Among various types of metamodels, the Gaussian Process (GP) model is popular for both deterministic and stochastic simulation optimization problems. However, input uncertainty is usually ignored in simulation optimization problems, and thus current GP-based optimization algorithms do not incorporate input uncertainty. This article aims to refine the current GP-based optimization algorithms to solve the stochastic simulation optimization problems when input uncertainty is considered. The comprehensive numerical results indicate that our refined algorithms with input uncertainty can find optimal designs more efficiently than the existing algorithms when input uncertainty is present.

Keywords:

• Gaussian process
• metamodel
• stochastic simulation optimization
• input uncertainty

Additional information

Notes on contributors

Haowei Wang

Haowei Wang is currently a Ph.D. candidate in the Department of Industrial Systems engineering and Management at the National University of Singapore. He received his B.Eng. degree in industrial engineering from Nanjing University in 2016. His research interests include uncertainty quantification, simulation metamodeling and optimization.

Jun Yuan

Jun Yuan is currently an associate professor with China Institute of FTZ Supply chain, Shanghai Maritime University. He received his B.E. degree in industrial engineering and management from Shanghai Jiao Tong University, Shanghai, P.R. China, in 2008, and a Ph.D. degree in industrial and systems engineering National University of Singapore, Singapore, in 2013. From 2014-2017, he worked as a research fellow at the National University of Singapore. His research interests include energy systems modeling, shipping energy systems, computer simulation, and machine learning.

Szu Hui Ng

Szu Hui Ng is an associate professor in the Department of Industrial Systems Engineering and Management at the National University of Singapore. She holds B.S., M.S., and Ph.D. degrees in industrial and operations engineering from the University of Michigan. Her research interests include computer simulation analysis and optimization, applications of simulation to maritime transportation and quality engineering. She is a member of IEEE and INFORMS, and a senior member of IISE.