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华师经管学术讲座第361期(管理)

2020-12-28 16:09:00 来源: 点击: 收藏本文

题目:Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform

时间:2020年12月28日(周一),15:00-16:30

地点:腾讯会议 (ID:945126889    密码:6688

主讲人:荣鹰教授

主持人:卿前恺副教授

主讲人简介:

荣鹰,现任上海交通大学安泰经济与管理学院教授、博士生导师。他于2010年8月回国执教于上海交通大学,此前在美国加州大学伯克利分校和里海大学从事科研工作,并在上海交通大学和美国里海大学分别获学士、硕士和博士学位。荣鹰教授主要研究领域为服务运营优化、供应链管理、新兴商业模型的运作以及数据驱动的优化模型。主持国家杰出青年科学基金、国家优秀青年科学基金等。研究成果发表在Management Science, Operations Research,Manufacturing & Service Operations Management,Production and Operations Management,Naval Research Logistics,IIE Transactions等国际顶级/权威学术刊物上。荣鹰教授获得过多次国际奖项,其中包括两度MSOM最佳论文奖和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 “late 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.

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