Student Seminar by Mr. Kaihang Zhang on Feb 27, 2025 4PM

Title: Tactical Operations of Service Region Dimensioning, Bundling, and Matching for On-Demand Food Delivery Services

Speaker: Mr. Kaihang Zhang (Department of Civil Engineering)

Date: Feb 27, 2025 (Thursday)

Time: 4:00 pm - 5:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: On-demand food delivery (OFD) services have experienced a significant surge in popularity in recent years, which poses various challenges for service operators. To address these challenges, we discuss an analytical model that captures the complex interplay of the OFD system by considering factors such as adjustable service region size and order bundling. We investigate how key decision variables, including maximum delivery distance and bundling ratio, affect the system's endogenous variables and two critical system performance metrics: customer total waiting time and order throughput. Our analysis yields several intriguing managerial insights. First, the maximum delivery distance has a non-monotonic impact on the customer accumulation time, delivery time, and total waiting time, and there is a “win-win” situation in which increasing the maximum delivery distance benefits both the customer total waiting time and order throughput. Second, order bundling is crucial under high customer demand to ensure adequate food delivery supply, but it is less desirable under low customer demand due to increased detour distances in delivery. We further explore strategies for minimizing customer total waiting time (by setting small service regions and bundling ratios) and order throughput (by establishing larger service regions). Recognizing the partial conflict between these two objectives, we identify the Pareto-efficient frontier that serves as a guideline for service operators in balancing these competing goals.

About the speaker: Kaihang Zhang is currently a PhD candidate at The University of Hong Kong supervised by Dr. Jintao Ke. He is a student member of INFORMS, ITS of HKU, and the recipient of Hong Kong PhD Fellowship and HKU Presidential Scholarship. He has been developing economic analytical models and network flow models throughout his PhD study at HKU on the operations for on-demand urban mobility systems. During his time at HKU, he worked as a visiting research student at LIMOS, University of Michigan, working on the development of data-driven analytical model for food delivery services. He has Bachelor’s Degrees from Zhejiang University and UIUC, and a Master’s Degree from UC Berkeley.

Student Seminar by Dr. Zhuoye Zhang on Nov 29, 2024 (4PM)

Title: Optimal operation strategies of an urban crowdshipping platform in asset-light, asset-medium, or asset-heavy business format

Speaker: Dr. Zhuoye Zhang (Department of Data and Systems Engineering)

Date: November 29, 2024 (Friday)

Time: 2:00 pm - 3:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: This research investigates the operation strategies of an urban crowdshipping platform, which utilizes the latent capacity of the traveling ‘crowd’ in the transportation system to facilitate parcel delivery. We develop an analytical model to characterize the decision-making and operation strategies of a crowdshipping operator in alternative business formats (asset-light/medium/heavy). Asset-light platforms connect customers with potential carriers in the crowd without involving delivery assets, whereas asset-medium and asset-heavy operators integrate crowd carriers with outsourced or owned delivery fleets, respectively. In particular, we first formulate the two-sided market equilibrium of crowdshipping system on account of customers’ willingness to use and crowds’ willingness to serve. Based on the market equilibrium, the crowdshipping operator’s optimal strategies in terms of pricing and/or fleet sizing are identified for profit-maximization or social welfare-maximization in alternative business formats. We show that the introduction of crowdshipping can simultaneously improve the benefits of logistics customers, the crowd, and the crowdshipping platform operator, leading to a win-win-win outcome. Furthermore, we establish analytical conditions for one business format being superior to another. We find that if the externality (or marginal social cost) of an unmatched order is smaller in a particular business format, it will result in larger consumer surplus for customers, greater net benefit for crowd carriers, and more profit for crowdshipping operator. Under mild conditions, the crowdshipping operator adopting the asset-light or asset-medium format can earn a positive profit at the social optimum.

About the speaker: Dr. Zhuoye Zhang is currently a postdoctoral researcher at the Department of Data and Systems Engineering, The University of Hong Kong (HKU). He received his Ph.D. degree in transportation engineering from HKU in 2024, his master's degree from Shanghai Jiao Tong University, and his bachelor's degree from Tongji University. His research interests include transportation economics, transportation network modeling and optimization, mainly focusing on the analysis of shared, automated and electrified transportation and logistics services. His work has been published in leading academic journals and conferences, including Transportation Research Part B/C, the International Symposium on Transportation and Traffic Theory (ISTTT), the Transportation Research Board (TRB) Annual Meeting, etc. Dr. Zhang is also a recipient of the Best Paper Award of the 5th Frontier Symposium on Traffic Behavior and Transportation Science (TBTS 2024), and the 14th Workshop on Computational Transportation Science (CTS).

Academic Exchange Seminars with Tongji University Delegation on Oct 16, 2024

Invited by ITS Fellow Dr. Jintao Ke, a delegation of 2 teachers and 14 research students from the College of Transportation Engineering, Tongji University visited the Department of Civil Engineering of HKU on Oct 16, 2024. In the 2-hour academic sharing session, Yijun Liu (Tongji University), Jian Liang (HKU), Hao Zhong (Tongji University), Taijie Chen (HKU), Weihan Bi (Tongji University), and Jiguang Wang (HKU) presented their studies. Each presentation was followed by a Q&A session for in-depth discussions about the research. Dr. Jintao Ke was presented a souvenir from the delegation.