Student Seminar by Mr. Kaihang Zhang on Apr 20, 2026, 4:00PM

Title: Let Drivers Roam: How ETA Slack Unlocks Network Effects in On-Demand Food Delivery Markets

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

Date: Apr 20, 2026 (Monday)

Time: 4:00 pm - 5:00 pm

Venue: Room 8-28, Haking Wong Building, The University of Hong Kong

Registration Link: https://forms.cloud.microsoft/r/bMhERMSx61

ITS Student Committee will provide a beverage for registered participants.

Abstract: Platforms in on-demand delivery compete on speed by committing to tight estimated time of arrival (ETA) targets. Tight ETAs reduce drivers’ flexibility while busy, limiting order bundling and cross-neighborhood switching; this fragments the implicit service network and restricts pooling benefits. We develop a parsimonious steady-state model that links an ETA commitment to endogenous bundling, routing and repositioning workload for multi-stop tours, and a switching probability that captures whether drivers can relocate to adjacent restaurant clusters between bundles. These endogenous variables feed into a mean-field (aggregate level) queuing approximation for pooled matching, yielding tractable expressions for assignment-delay metrics. Within an operating range we call the active regime, relaxing ETA commitments increases bundling and the probability that a busy driver can accept a new assignment, reduces per-order service time, and lowers assignment delay. We show that tight ETAs fragment the cluster network, loosing pooling benefits. We then introduce a fleet-based price of urgency that quantifies the additional fleet required to meet a target assignment delay when operating at tighter ETAs relative to a benchmark, highlighting a nonlinear fleet cost near the connectivity threshold where switching becomes insufficient to maintain network connectivity.

Bios: Kaihang is a PhD student at the Department of Civil Engineering, HKU, supported by HKPF and HKUPS. His research interests include modeling of complex systems, optimization under equilibrium constraints, and behavioral analysis and operations. Prior to HKU, he worked on traffic state analysis and public transportation system design. He holds bachelor’s degrees from ZJU and UIUC, and a master’s degree from UC Berkeley.

Student Seminar by Mr. Yuzhen Feng on Mar 26, 2026, 3:30PM

Title: Resource-Constrained User Equilibrium

Speaker: Mr. Yuzhen Feng (The Hong Kong Polytechnic University)

Date: Mar 26, 2026 (Thursday)

Time: 3:30 pm - 4:30 pm

Venue: Room 8-28, Haking Wong Building, The University of Hong Kong

Registration Link: https://forms.cloud.microsoft/r/552KT3frUg

ITS Student Committee will provide a beverage for registered participants.

Abstract: We study user equilibrium with resource constraints (UERC) where users face strict budget on energy, time, risk, etc., motivated by applications in urban mobility, perishable logistics, and itinerary planning. UERC unifies user equilibrium and the resource‑constrained shortest path problem by allowing both travel costs and resource consumption to depend on congestion, making path feasibility endogenous. We formulate UERC as a quasi‑variational inequality and prove that a UERC exists when every user has at least one feasible path regardless of congestion; otherwise, deciding existence is NP-hard. We establish uniqueness in networks with parallel links between a single origin-destination pair, even with heterogeneous budgets among users, and show counterexamples where minimal generalizations destroy uniqueness. When cost equals resource consumption on every link, UERC coincides with classical user equilibrium and thus inherits its uniqueness of link flows. When cost and resource consumption differ but their ratio is uniformly bounded above and below, both the price of anarchy and unfairness (the max-to-min ratio of equilibrium costs experienced by the same user type) are bounded; without such comparability, both can be unbounded. We also reveal a paradoxical comparative statics: increasing users' budgets can raise total system cost by allowing more users to already congested routes. Computationally, we develop a penalty‑based algorithm with column generation that repeatedly solves a resource‑constrained shortest path subproblem, and prove convergence under the same sufficient condition for existence. Experiments on benchmark road networks demonstrate the computational scalability of our algorithm and quantify the impact of endogenous feasibility on equilibrium outcomes.

Bios: Mr. Yuzhen Feng is currently a third-year Ph.D. student in Transportation at The Hong Kong Polytechnic University, supervised by Dr. Wei Liu. His recent work is mainly related to transportation network modeling, optimization, and equilibrium. He received his B.Mgt. degree in Information Management and Information System from the School of Economics and Management at Tongji University in 2023. During his undergraduate studies, he also worked with Prof. Xiaolei Wang on dynamic en-route ride-pooling. He is a recipient of the Hong Kong Ph.D. Fellowship.

18 Mar 2026 (Wed) 10:00-12:00 | Seminar by Prof. Stefan Minner

Decompose-route-improve framework for solving large-scale vehicle routing problems with time windows

Speaker:

Prof. Stefan Minner

School of Management, Technical University of Munich (TUM)

Date:    Mar 18, 2026 (Wednesday)

Time:   10:00 am – 12:00 nn

Venue: The Tam Wing Fan Innovation Wing Two, The University of Hong Kong

Abstract

Several metaheuristics use decomposition and pruning strategies to solve large-scale instances of the vehicle routing problem (VRP). Those complexity reduction techniques often rely on simple, problem-specific rules. However, the growth in available data and advances in computer hardware enable data-based approaches that use machine learning to improve scalability of solution algorithms. We propose a decompose-route-improve (DRI) framework, which first partitions the customers of the VRP with time windows (VRPTW) using clustering. Its dissimilarity metric incorporates customers’ spatial, temporal, and demand data and is formulated to reflect the problem’s objective function and constraints. Second, the resulting sub-routing problems are solved independently using any suitable algorithm. Lastly, we apply pruned local search (LS) between solved subproblems to improve the overall solution. Pruning is based on customers’ similarity information obtained in the decomposition phase. In a computational study, we parameterize and compare existing clustering algorithms and benchmark the DRI against a state-of-the-art solver on large VRPTW instances and very large-scale instances with up to 30,000 customers, which are introduced in this study. Results show that our data-based approach outperforms classic and recent cluster-first, route-second approaches as well as decomposition strategies that are solely based on customers’ spatial information. The newly introduced dissimilarity metric forms separate sub-VRPTWs and improves the selection of LS moves in the improvement phase. Thus, the DRI scales existing metaheuristics to achieve high-quality solutions faster for very large-scale VRPTWs by efficiently reducing complexity. Further, the DRI can be adapted to various solution methods and problem characteristics, such as the distribution of customer locations and demands, the depot location, and different time window scenarios, making it a generalizable approach to solving large- and very large-scale practical routing problems.

Following the research presentation, Professor Minner will also share his insights and experiences regarding research publications, drawing from his role as Editor-in-Chief of the International Journal of Production Economics.

About the speaker

Stefan Minner is a Full Professor for Logistics and Supply Chain Management at the School of Management, Technical University of Munich (TUM) and a core-member of the Munich Data Science Institute (MDSI). Currently, he is Vice Dean of Research and Innovation at TUM School of Management. His research interests using methods of operations research, artificial intelligence and machine learning are in global supply chain design, transportation optimization and inventory management. His work was published in many peer reviewed journals, including Management Science, Manufacturing & Service Operations Management, Operations Research, Production and Operations Management, Transportation Science, Transportation Research Parts B, C and E, European Journal of Operational Research, and the International Journal of Production Research. For his research output, he is currently listed among the top 1% business professors in German speaking countries by Wirtschaftswoche and among the top 2% researchers worldwide in a citation-based ranking by Stanford University. He serves on several editorial boards of logistics and operations research journals. Currently, Stefan Minner is the Editor-in-Chief of the International Journal of Production Economics. Stefan Minner is a fellow of the International Society for Inventory Research (ISIR) and the International Foundation of Production Research (IFPR). He received the PhD supervisory award at TUM School of Management in 2024 and the Science Award for his lifetime achievements by the German Operations Research Society in 2025.