【主講】Hai Wang,新加坡管理大學助理教授

【主題】網約車平台中的匹配與定價策略研究

【時間】2018年12月26日（周三）10:00-12:00

【地點】清華經管學院偉倫樓453

【語言】英語

【主辦】管理科學與工程系

【簡曆】Hai Wang老師的簡曆

Hai Wang is an Assistant Professor in the Area of Intelligent Systems & Optimization at Singapore Management University. He held a bachelor degree from Tsinghua University, dual Master’s degree in operations research and transportation from MIT, and Ph.D. from MIT Operations Research Center. His research has focused on methodologies on operations research, data-driven modelling, computational algorithms, and machine learnings, and the applications in urban, transportation, and logistics systems. Particularly, he is interested in on-demand service and shared transportation systems. He also explores research in e-commerce and healthcare analytics. He has papers published in “Transportation Science”, “American Economic Review Papers & Proceedings”, “Manufacturing & Service Operations Management”, and “Transportation Research Part B”. During his Ph.D. at MIT, he served as the co-President of MIT Chinese Students & Scholars Association and Chair of MIT-China Innovation and Entrepreneurship Forum.

【Speaker】Hai Wang, Assistant Professor，Singapore Management University

【Topic】Matching and Pricing Coordination for Ride-Sharing Platforms

【Time】Wednesday, Dec. 26, 2018, 10:00-12:00

【Venue】Room 453, Weilun Building, Tsinghua SEM

【Language】English

【Organizer】Department of Management Science and Engineering

【Abstract】With the rapid development and popularization of mobile and wireless communication technologies, dynamic ride-sharing platforms, as pioneers in a sharing economy context, provide on-demand shared transportation services and are disruptively changing the transportation industry. First, we study the matching problem for ride-sharing platforms, in which the platforms match passengers and drivers in real time considering multiple objectives such as platform revenue, pick-up distance, and service quality. It is a multi-period multi-objective online optimization problem. We develop an efficient online matching policy that adaptively balances the trade-offs between multiple objectives in a dynamic setting and provide theoretical performance guarantee. We prove that the proposed adaptive matching policy can achieve the “target-based optimal solution”, i.e., the solution that minimizes the Euclidean distance to any pre-determined multi-objective target, and demonstrate its good performance through numerical experiments and implementation using real data from a ride-sharing platform. Second, we propose a framework to study the coordination of platform demand and supply using prices and wages. The platforms use earnings-sensitive drivers with heterogeneous reservation wage that serve time- and price-sensitive passengers with heterogeneous valuations of the service. We propose a queueing model with endogenous supply and endogenous demand and present steady-state performance in equilibrium to determine the optimal price and wage that maximize the platform’s profit or social welfare.