PhD Dissertation Defense by ZHANG Zhiqin | Rich Models and Methods for On-Demand Same Day Deliveries

Rich Models and Methods for On-Demand Same Day Deliveries

ABOUT THE TALK

Same-day delivery has brought numerous conveniences to people's lives, but it has also presented challenges in terms of service management. To effectively optimize on-demand same-day delivery operations within urban logistics, intelligent decision-making strategies capable of adapting to rapidly changing circumstances are essential. Employing effective decision-making strategies that account for order allocation, route planning, courier scheduling, and other relevant factors, is pivotal in advancing logistics operations, enhancing efficiency, customer satisfaction, and resource utilization in the context of dynamic same-day delivery problems.

The focus of this thesis revolves around different emerging challenges presented by on-demand same-day delivery problems, with a particular emphasis on dynamic problem scenarios and rich models. Our primary objective is to develop effective algorithms to accurately model and address these challenges. This thesis outlines this thesis in three specific domains: (1) addressing a same-day on-demand delivery problem that encompasses rich operating constraints; (2) tackling a same day delivery problem within a Peer-to-Peer (P2P) logistics platform incorporating rider preferences; (3) developing a end-to-end learning-based solution methods to solve a specialized dynamic pickup and delivery problem in instant delivery services.

SPEAKER BIOGRAPHY

Zhang Zhiqin is a PhD candidate in Computer Science at SCIS, supervised by Prof. Lau Hoong Chuin. His research interests lie at the intersection of operations research and artificial intelligence.