報告時間：2023年6月5日（星期一）10:00-11:30
報告平臺：騰訊會議 ID:429-228-652
報 告 人：高思陽 Associate Professor
工作單位：香港城市大學
舉辦單位：管理學院
報告簡介：
The ranking and selection (R&S) problem seeks to efficiently select the best simulated system design among a finite number of alternatives. It is a well-established problem in simulation-based optimization. In this research, we consider R&S in the presence of context, where the context corresponds to some side information to the simulation model. We utilize the OCBA approach to formulate the problem, design algorithms and conduct theoretical analysis. The performance of the proposed algorithm is demonstrated via a set of abstract and real-world problems.
報告人簡介：
Siyang Gao received the B.S. degree in Mathematics from Peking University in 2009, and the Ph.D. degree in Industrial Engineering from University of Wisconsin-Madison in 2014. Dr. Gao is an Associate Professor with the Department of Systems Engineering, City University of Hong Kong. His research is devoted to simulation optimization, machine learning and their applications in healthcare management. His work has appeared in top journals such as Operations Research, Production and Operations Management, INFORMS Journal on Computing, etc. Dr. Gao is the recipient of paper awards at several international conferences. He is a member of INFORMS and IEEE.
