主 题：ride-sourcing systems & multi-objective online ride-matching 共享交通系统与多目标优化匹配
主讲人：王海 新加坡管理大学信息系统学院的助理教授
卡内基梅隆大学海因茨信息系统与公共政策学院客座助理教授
点评嘉宾：
刘生龙 清华大学公共管理学院副教授
于 洋 清华大学交叉信息研究院助理教授
主持人： 赵静 清华大学公共管理学院副教授、cideg主任助理
时 间：2019年12月27日（星期五）14:00-16:00
地 点：清华大学公共管理学院302会议室
语 言：中文
主讲人介绍：
dr. wang is currently a visiting assistant professor at the heinz college of information systems and public policy at carnegie mellon university. he received a bachelor’s degree from tsinghua university, dual master’s degrees in operations research and transportation from mit, and a doctoral degree in operations research from mit. dr. wang is also an assistant professor in the school of information systems at singapore management university. his research has focused on methodologies of analytics and optimization, data-driven decision-making models, and machine learning algorithms, and their applications in smart cities and urban systems, including innovative transportation, advanced logistics, and intelligent healthcare systems. he has published in leading journals such as transportation science, american economic review papers & proceedings, manufacturing & service operations management, and transportation research part b: methodological. dr. wang serves as the guest-editor for the special issue on innovative shared transportation in transportation research part b, as a reviewer for over 25 different academic journals, and has been named a chan wui & yunyin rising star fellow in transportation. he was nominated for the goodwin medal, mit’s top teaching award for graduate students and has been awarded the excellent teaching award for junior faculty at singapore management university. during his ph.d. studies at mit, he also served as the co-president of the mit chinese students & scholars association and as chair of the mit-china innovation and entrepreneurship forum.
内容摘要：
we propose a general framework to study the on-demand shared ride-sourcing transportation systems and summarize corresponding research problems and methodologies. we then focus on multi-objective matching between demand and supply. the platforms match passengers and drivers in real time without observing future information, considering multiple objectives such as pick-up time, platform revenue, and service quality. we develop an efficient online matching policy that adaptively balances the trade-offs between multiple objectives in a dynamic setting and provide theoretical performance guarantees for the policy. we prove that the proposed adaptive matching policy can achieve the solution that minimizes the euclidean distance to any pre-determined multi-objective target. through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach is able to arrive at a delicate balance among multiple objectives and bring value to all the stakeholders in the ride-sourcing ecosystem.
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