PhD Dissertation Proposal by YANG Jingfeng | Data-driven optimization approaches for dynamic urban logistic operational problems

With rapid urbanization, urban logistics delivery operations should be optimized for capacity and efficiency. Various optimization approaches for solving urban logistics problems such as routing and scheduling in both static and dynamic settings have been proposed in recent decades. Recently, with the increasing computing power and the prolific application of machine learning in recent years, more research is being conducted to better understand how data and machine learning can be better integrated into traditional urban logistics optimization models. Furthermore, the pervasive use of smart phones and internet innovations raise new research challenges in urban logistics, such as collaboration in last-mile delivery and on-demand food delivery services.

My PhD research is motivated by these emerging problems and my focus is to investigate data-driven optimization approaches. In this proposal, I will discuss my work in two domains: (1) collaborative urban delivery with alliance and (2) dynamic urban area sizing for on-demand food delivery service.

Jingfeng Yang received the B.E. degree in transportation engineering from Shanghai Jiao Tong University, China in 2015 and M.S. degree in 2018. Now he is pursuing the Ph.D. degree in Computer Science in Singapore Management University. His research focuses on the optimization for urban logistics.