报告时间：2023年6月5日（星期一）上午10：00-11:30

报告地点：线上报告（腾讯会议：429-228-652）

报告人：高思阳

工作单位：香港城市大学

举办单位：合肥工业大学管理学院

报告简介：

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.

报告人简介：

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.