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

Published in Transportation Research Part C: Emerging Technologies, 2025

Recommended citation: Zhang, Kaihang, Jintao Ke, Hai Wang, and Yafeng Yin. 2025. "Tactical Operations of Service Region Dimensioning, Bundling, and Matching for On-Demand Food Delivery Services." Transportation Research Part C: Emerging Technologies 174 (May): 105069. https://doi.org/10.1016/j.trc.2025.105069.

Abstract

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, this paper presents 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, namely the 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 a Pareto-efficient frontier that serves as a guideline for service operators in balancing these competing goals.

History

Early versions of this work has been presented in HKSTS 2023, TRB 2024, HKURPS 2024, and the student seminar of HKUITS (2025 Feb).