报告人（单位）

潘凯（香港理工大学）

点评人（单位）

金子亮（性爱影片 ）

时间地点

时间：2025年9月12日10:30

地点：腾讯会议（867-169-926，实名参加）

报告内容：

Using electric vehicles (EVs) with vehicle-to-grid (V2G) technology in a shared mobility system promotes sustainability but limits vehicle accessibility. This highlights the importance of optimizing the initial EV allocation, which should also consider subsequent operational decisions. The problem is further complicated by correlated uncertainties in trip demands across service regions and time periods without perfect knowledge of these correlations. We propose a two-stage distributionally robust optimization (DRO) model considering ambiguously correlated trip-demand uncertainties. In the first stage, an operator decides the initial vehicle allocation. In the second stage, the operator determines various operational decisions to meet demands over a time horizon. The objective is to minimize the expected cost under a worst-case joint distribution within an ambiguity set based on moment information of the correlated uncertainties. We show a monotonic relationship between the optimal objective value and the trip-demand covariance matrix. We further prioritize trip-demand pairs based on their covariance value or shadow price, enabling us to focus on a subset of demand pairs, which is especially appealing given the operator’s limited resources. To improve computational efficiency, we propose approximations of the DRO model based on principal component analysis and develop a hybrid algorithm using a temporal decomposition technique. Numerical results based on real data confirm our approach's efficiency. We show that it is crucial to properly incorporate correlation information, which can attain up to 4.66% total cost reduction. Furthermore, EVs mostly charge during the early hours when electricity prices and trip demands are low and discharge when prices are high. The peaks of relocation deviate from the peaks of charging of EVs. Faster charging reduces the EV allocation and total cost. We observe more frequent charging of EVs under a time-based pricing scheme for charging compared to an amount-based pricing scheme.

报告人简介：

Kai Pan is currently an Associate Professor in Operations Management at the Faculty of Business of The Hong Kong Polytechnic University (PolyU), the Director of the MSc Program in Operations Management (MScOM), and the Deputy Director of the Doctor of Business Management (DBM) Program. He serves as a Secretary/Treasurer for the INFORMS Computing Society (ICS) and an Associate Editor for IISE Transactionsand Omega. He received his Ph.D. degree from the University of Florida, USA, in 2016 and his Bachelor's degree from Zhejiang University, China, in 2010. Before he joined PolyU in 2016 right after his Ph.D., he worked as a Research Scientist at Amazon (Seattle, Washington) on Supply Chain Optimization and a Power System Engineer at GE Grid Solutions (Redmond, Washington) on Electricity Market Operations. His research interests include Stochastic and Discrete Optimization, Robust and Data-Driven Optimization, Dynamic Programming, and their applications in Energy Market, Smart City, Supply Chain, Shared Mobility, Marketing, and Transportation. His research on these topics has been published in Operations Research, Manufacturing and Service Operations Management, INFORMS Journal on Computing, Production and Operations Management, IISE Transactions, European Journal of Operational Research, IEEE Transactions on Power Systems, Transportation Research Part B, etc. He was the first-place winner of the prestigious IISE Pritsker Doctoral Dissertation Award in 2017 and the awardee of the PolyU Young Innovative Researcher Award (YIRA) 2025.