PhD Dissertation Proposal by ZHANG Zhiqin | Rich models and methods for on-demand same day deliveries

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. This research is driven by these emerging challenges in on-demand same-day delivery problems which are rich in operational constraints and driver-behavioral preferences, and the primary objective is to model these problems and develop efficient algorithms that leverage heuristics and historical data. This proposal outlines my work in two specific domains: (1) addressing a same-day delivery problem that encompasses realistic operating constraints, and (2) tackling a rider-centered decision-making problem within a Peer-to-Peer (P2P) logistics platform.

Zhiqin Zhang is currently pursuing a Ph.D. in Computer Science at Singapore Management University. His doctoral journey began in 2020 under the guidance of Professor Lau Hoong chuin. Prior to this, he earned his Bachelor's degree in Chemical Engineering from Sichuan University and completed his Master's degree in Process System Engineering at Carnegie Mellon University. Zhiqin's research primarily revolves around the integration of operations research and artificial intelligence. He has published as the first author in publications such as Transportation Research Part E: Logistics and Transportation Review and the International Conference on Computational Logistics.