華師經(jīng)管學術(shù)講座第361期（管理）
題目：Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform
時間：2020年12月28日（周一），15:00-16:30
地點：騰訊會議 （ID：945126889 密碼：6688）
主講人：榮鷹教授
主持人：卿前愷副教授
主講人簡介：
榮鷹，現(xiàn)任上海交通大學安泰經(jīng)濟與管理學院教授、博士生導師。他于2010年8月回國執(zhí)教于上海交通大學，此前在美國加州大學伯克利分校和里海大學從事科研工作，并在上海交通大學和美國里海大學分別獲學士、碩士和博士學位。榮鷹教授主要研究領(lǐng)域為服務運營優(yōu)化、供應鏈管理、新興商業(yè)模型的運作以及數(shù)據(jù)驅(qū)動的優(yōu)化模型。主持國家杰出青年科學基金、國家優(yōu)秀青年科學基金等。研究成果發(fā)表在Management Science, Operations Research，Manufacturing & Service Operations Management，Production and Operations Management，Naval Research Logistics，IIE Transactions等國際頂級/權(quán)威學術(shù)刊物上。榮鷹教授獲得過多次國際獎項，其中包括兩度MSOM最佳論文獎和INFORMS Energy, Natural Resources & Environment Young Researcher Prize。
摘要：
The focus of this talk is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we find empirical evidence that an “early delivery” is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with “l(fā)ate deliveries” is much stronger than the positive effect associated with early deliveries. Moreover, we show empirically that a driver’s individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on customer future orders) into their operations.
歡迎感興趣的師生積極參加！