Equitable Delivery Zoning for Last-Mile Logistics: A Framework Validated with Implementation

Problem definition: Parcel logistics companies use zoning systems to manage last-mile delivery operations. This practice divides a service area into zones, each served by its own station and drivers. Designing an optimal zoning policy is challenging because the practical service area often includes many customer locations, and vehicle routing problems (VRPs) should be incorporated as a subroutine. Existing methods have limitations in modeling practical fleets with diverse vehicle types and broader routing objectives. Methodology/results: We collaborated with a delivery company to develop a novel data-driven zoning method that minimizes the maximum work span of delivery stations. We define the work span of a station as the duration between the start time of sorting the first parcel and the return time of the last driver upon finishing all assigned delivery tasks. Our method iteratively solves VRPs using observed demand data and partitions the region with additively weighted Voronoi diagrams. We leverage the primal-dual properties and develop a subgradient algorithm with established convergence conditions. Our numerical analyses show that this approach not only reduces the station-level maximum average work span by 20.5% and the average delivery time per driver by 17%, but it also reduces their standard deviations by approximately 25% and 19%, respectively. When tested in actual field conditions, we continue to observe reductions in the work span of the stations and the delivery time of the drivers. Managerial implications: Our approach reduces delivery lead times, better distributes workload among drivers, and limits long working hours, creating a win–win outcome for both the company and its drivers. Besides improving service quality and driver well-being, we estimate annual savings of nearly half a million dollars simply by readjusting the boundaries of service zones. The proposed framework can also be applied in other spatial service settings to achieve equitable distribution of workload among resources. Funding: J. G. Carlsson acknowledges funding support from DARPA [Grant HR0011-25-3-0234] and USDOT [Grant USC-DOT-1217]. S. F. W. T. Lim acknowledges funding support from the Eli Broad College of Business 2024 Summer Research Grant. S. Liu acknowledges funding support from the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-04950]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1221 .