管理科学与工程系学术讲座系列2021年第11讲

主 题：Data-Driven and People-Centric Optimization for Last-Mile Delivery Operations Management

主讲人: 刘晟（多伦多大学罗特曼管理学院）

时 间：2021-10-13 10:00

地 点：Zoom会议室

语 言：中英文

地  点：Zoom会议室

https://zoom.us/j/81030133994?pwd=YURIQ3p1L1ltbklxL1NqOW93SzZhQT09 

会议号：810 3013 3994

密  码：529149

讲座摘要：

This talk presents my recent projects on last-mile delivery operations management. The first project is devoted to order assignment optimization for on-time last-mile delivery. Working with a food delivery service provider, we develop a framework that effectively integrates the driver's routing behavior into the stochastic and robust optimization models. We illustrate that learning the routing behaviors of drivers can lead to reduced delivery delays for customers. The second project studies the optimal region partitioning policy to minimize the expected delivery time of customer orders in a stochastic and dynamic setting. This policy assigns every driver to a subregion, hence making sure drivers will only be dispatched to their own territories. We characterize the structure of the optimal partitioning policy and show its expected on-time performance converges to that of the flexible dispatching policy in heavy traffic. We devise partitioning algorithms that are guaranteed to find the optimal partition efficiently.

主讲人简介：

Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. His research interests lie in smart city operations (especially transport, logistics, and sustainable infrastructure), data analytics, and optimization. His research has been published in Management Science, Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, and IEEE journals. He received a PhD in Operations Research from UC Berkeley in 2019 and a BSc in Industrial Engineering from Tsinghua University in 2014. He has contributed to the development of advanced decision-making models for leading companies, including Amazon, Lyft, JD.com, and CNPC.