报告一：动态拼车前瞻性派单策略设计

主讲人：王晓蕾，同济大学经济与管理学院教授

报告时间：11月14日（周四下午）15：00-17：00

报告地点：英国正版365官方网站大楼318会议室

报告摘要：

The fast development of information technologies in recent decade has greatly facilitated large-scale implementation of dynamic ridepooing services, e.g., Uber Pool, Didi Pinche. In dynamic ridepooling services, service providers respond to on-demand mobility requests immediately, dispatch (vacant or partially occupied) vehicles in real-time, and keep searching for matching orders along the trip. Most existing dispatching strategies ignore forthcoming matching opportunities, therefore having short-sighted limitations. In this paper, we argue that the system may benefit from strategically giving up certain current matches, and propose a probabilistic matching policy under which appeared matching opportunities are accepted with varying probabilities. Assuming that each ridepooling passenger shares vehicle space with at most one another during the entire trip, and ridepooling orders between each OD pair appear following a Poisson process with a given rate in each study period, we propose a system of nonlinear equations to predict the system performance and the matching potential of each OD pair under any probabilistic matching policy. Based on the model, we then propose an efficient solution algorithm to optimize the probabilistic matching policy (i.e., the acceptance probability of each match) for minimal expected total ride distance per unit period. The optimized probabilistic matching policy allows us to make decisions encompassing a consideration of all potential matches during each trip; therefore, it has a forward-looking feature. Through simulation experiments conducted on grid networks and the real road network of Haikou (China) utilizing a real order dataset, we demonstrate that our model yields accurate predictions of the average ride/shared distance for each origin-destination (OD) pair across various matching policies. Furthermore, the optimal matching policy generated by our method can reduce the average total ride distance per unit period by over 5% when demand is high.

报告人简介：

王晓蕾，同济大学经济与管理学院教授。2008年本科毕业于中国科技大学（获郭沫若奖学金），2012年博士毕业于香港科技大学（获HKUST SENG PhD Research Excellence Award）。一直致力于城市交通系统优化领域的研究，主要研究兴趣：共享出行服务运营优化以及共享出行下的城市交通管理。在交通领域主要SCI/SSCI期刊发表论文30余篇，其中14篇发表于交通领域顶刊Transportation Research Part B、Transportation Science，篇均引用80+；主持国家自然科学基金重点、优青、面上和青年项目，创新群体“综合运输系统运营管理”项目骨干成员；世界交通运输大会共享与预约出行技术委员会主席，管理科学与工程学会交通运输管理分会委员，交通领域主要期刊Transportation Research Part E编委。

报告二：Revenue Management Under a Price Alert Mechanism

主讲人：江波，上海财经大学教授

报告时间：11月14日（周四下午）15：00-17：00

报告地点：英国正版365官方网站大楼318会议室

报告摘要：

Many online platforms adopt a price alert mechanism to facilitate customers tracking the price changes. This mechanism allows customers to register their valuation to the system when they find the price is larger than the valuation on their arrival period. Once the price drops below the customers’ registered price, a message will be sent to notify them. In this paper, we study the optimal pricing problem under this mechanism. First, when the customer's waiting time is one period, we show that it is optimal for the seller to use a threshold to decide whether to accept or reject a registered price, and the price trajectory under the optimal policy has a stochastic cyclic decreasing structure. When the customer’s valuation is a uniform distribution, the analytical form of the optimal policy is further obtained. When the customer’s patience level is two periods, we obtain the structure of the optimal policy by showing the asymmetric role each registered price plays in the optimal policy. Then we consider the case when the customer can stay in the system for an infinite number of periods. We derive an asymptotic optimal policy for this case. We find that adopting the price alert mechanism always increases social welfare; however, it may hurt the customer surplus when the seller has a large discount factor. Finally, we consider the case when the customers can strategically react to the price alert mechanism by timing their purchases and reporting false valuations. Using a Stackelberg’s game model, we obtain the seller’s optimal threshold type of policy. We show that the price alert mechanism can still be helpful to the seller, although the advantage diminishes when customers are very strategic.

报告人简介：江波，美国明尼苏达大学博士，上海财经大学信息管理与工程学院/交叉科学研究院常聘教授、副院长；国家级青年人才、上海市高层次人才。从事运筹优化、收益管理、机器学习等方向的研究。成果发表于运筹优化与机器学习的国际顶级期刊《Operations Research》、《Mathematics of Operations Research》、《Mathematical Programming》、《INFORMS Journal on Computing》、《SIAM Journal on Optimization》、《Journal of Machine Learning Research》。获得了中国运筹学会青年科技奖、上海市自然科学奖二等奖、宝钢优秀教师奖等荣誉。主持多项国家自然科学基金项目包括自科重大项目课题。为顺丰、京东等国内多个标杆企业提供仓库优化、智能定价、智能选址等技术服务。