PhD Dissertation Defense by YANG Jingfeng | Data-Driven Optimization Approaches for Dynamic Urban Logistics Operational Problems

Given the rapid pace of urbanization, there is a pressing need to optimize urban logistics delivery operations for enhanced capacity and efficiency. Over recent decades, a multitude of optimization approaches have been put forth to address urban logistics challenges, encompassing routing and scheduling within both static and dynamic contexts. In light of the rising computational capabilities and the widespread adoption of machine learning in recent times, there is a growing body of research aimed at elucidating the seamless integration of data and machine learning within conventional urban logistics optimization models. Additionally, the ubiquitous utilization of smartphones and internet innovations presents novel research challenges in the realm of urban logistics, notably in the domains of last-mile delivery collaboration and on-demand food delivery services.

My PhD research is driven by these new demands, exploring how data-driven methods can improve urban logistics. This thesis will encompass a comprehensive discussion of my research conducted in three key domains: (1) collaborative urban delivery with alliances; (2) dynamic service area sizing optimization for on-demand food delivery services; and (3) optimization of dynamic matching time intervals for on-demand food delivery services.

Jingfeng Yang is a PhD candidate in Computer Science in Singapore Management University, supervised by Prof. LAU Hoong Chuin. Jingfeng Yang conducts research on the intersection between Operations Research and Machine Learning, specifically on designing and exploring data-driven optimization approaches to solve challenging operational problems in urban logistics. Prior to joining SMU, Jingfeng graduated with a Bachelor's and Master's degree both in Transportation Engineering in Shanghai Jiao Tong University.