时间：2026年4月20日 09:30-11:30

地点：东校区财科馆317会议室

【讲座一】

题目：计算设计科学研究探索：以参照点依赖的用户细粒度偏好学习为例

时间：09:30-10:30

主讲人：吴俊杰，男，1979年生人，北京航空航天大学经济管理学院/网络空间安全学院双聘教授（二级岗），管理科学与工程学科责任教授，数据智能与智慧管理工信部重点实验室主任，国家级领军人才。本硕博毕业于清华大学，获工学学士学位和管理学博士学位。获全国百篇优秀博士学位论文、三项省部级科技一等奖、全国科技系统抗疫先进个人、中国发明协会创业人物奖等荣誉。担任国务院学位委第八届学科评议组（管理科学与工程）成员，国家自然科学基金委重大研究计划（大数据驱动的管理与决策）指导专家组成员，管理科学与工程学会“人工智能技术与管理应用”分会主任委员，《管理科学学报》等多个期刊领域编辑。长期从事管理科学、信息科学、社会科学的交叉创新研究，主要研究兴趣为数据挖掘、机器学习、信息系统，提出并推动“数据智能”研究，主持国家级重大重点项目4项，发表MISQ、ISR、TKDE、TDSC、SIGKDD、SIGIR等管理与信息类顶级期刊和会议论文近40篇，获国家发明专利授权20余项，研发通用/专用数据智能平台系统10余套，成果应用于智慧城市、社会治理、金融科技、商业智能、智慧医疗等相关行业和政企机构，政策建议获国家高层采纳。

【讲座二】

题目：Managing Shared Mobility Systems with Electric Vehicles Under Correlated Demand Uncertainties

时间：10:30-11:30

摘要Abstract:

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 develop a hybrid algorithm by proposing approximations of the DRO model based on principal component analysis and a temporal decomposition technique. Numerical results based on real data confirm our approach's efficiency. It is crucial to properly incorporate correlation information, which can attain a significant 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.

主讲人简介Bio:

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 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, Telecommunication, and Marketing. 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 IISE Pritsker Doctoral Dissertation Award in 2017 and the awardee of the PolyU Young Innovative Researcher Award (YIRA) 2025. He serves as an Associate Editor for IISE Transactions, Decision Sciences, and Omega, and has served as a Secretary/Treasurer for the INFORMS Computing Society (ICS).