Data Analytics for Order Assignment in Last Mile Delivery with Sheng Liu, Berkeley

February 15, 2019


This talk is open to MIE faculty and graduate students. Registration is not required.

Food delivery market is rising across the world. Retailing giants and chain restaurants are providing fast food delivery service from their physical stores. A key challenge for them is to improve the on-time performance of delivery services. Working with a major food delivery service provider in China, we develop a data-driven optimization framework to optimize the order assignment decision. Driven by the real-world data set, we propose a machine learning approach to predict the actual travel distance considering drivers’ behaviors. Combined with the travel distance prediction, our optimization framework is flexible and yields significantly better result than existing models that assume drivers follow the shortest-distance routes.

Sheng Liu is a PhD candidate in the Department of Industrial Engineering and Operations Research from the University of California, Berkeley. Prior to Berkeley, he earned a BEng in Industrial Engineering from Tsinghua University. His research interests include data analytics, supply chain management, and transportation. His work involves collaborations with both the private and public sectors, including the China National Petroleum Corporation and the Zhuhai Urban Planning Institution.