On-Demand Meal Delivery Platforms: Operational Level Data and Research Opportunities
Wenzheng Mao, Liu Ming, Ying Rong, Christopher S. Tang, Huan Zheng
Abstract
This paper describes the operations of most on-demand meal delivery platforms and discusses how empirical research can improve the operational performance of these platforms. To support and encourage more studies on the operations of on-demand delivery platforms, we provide a unique data set obtained from a meal delivery platform in China. This data set contains operational level data sampled from July 1 to August 31, 2015, in Hangzhou, China. The data set includes information about order placements, order deliveries, restaurants, drivers, weather and traffic conditions, and so on. We also review recent studies on meal delivery platforms and suggest research opportunities for improving delivery performance.