Seminars topic: In this talk, I will present my recent work on retail and last-mile delivery systems, driven by industry collaborations.

In the first part, I will discuss the retail prediction challenges faced by JD.com and our explorations in data-driven demand modeling.

The second part of the talk will describe two field implementations of new optimization algorithms to improve operational efficiency:
(a) delivery zoning, and
(b) menu design for meal delivery platforms.

In particular, in study (b), we developed a new optimization model that connects delivery service operations with assortment planning decisions. This model has been validated through a field experiment in the Vancouver metropolitan area.

Scholars Background: Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. He joined Rotman after graduating from Berkeley in 2019. Sheng’s research focuses on solving operations problems in supply chains, transportation, and logistics systems through optimization and data analytics. His industry experience includes consulting or working for organizations such as JD.com, Sport Chek, Ninja Van, Hungerhub, Amazon, and Lyft. His work has been recognized by several awards and paper competitions, including the INFORMS Public Sector Operations Research Best Paper Award, INFORMS TSL Outstanding Paper Award (Freight Transportation and Logistics), and M&SOM Data-Driven Research Competition. He currently serves as an associate editor of Transportation Science and an Editorial Review Board member of Service Science.

Time and Location: July 11, 2025, 10:00–11:30, Room A723, School of Management

Language: Bilingual (ENG & CN)