Optimization of Last-Mile Logistics Delivery Routes for Ground-Vehicle and Drone Parallel Distribution from Pre-Warehouses Considering Customer Priorities

Pre-warehouse last-mile delivery is currently constrained by service radiuses and intense delivery pressures. Meanwhile, national policies are increasingly promoting a transition toward green logistics. By undertaking deliveries to remote or dispersed locations, UAVs can streamline truck routes and minimize the fuel consumption and emissions typically exacerbated by urban traffic congestion. Accordingly, this paper establishes a Ground-Vehicle and Drone Parallel Distribution Model with Priorities (PW-PDSVRP-P), quantifying customer priorities via delivery delay functions to align efficiency with social service requirements. A master–slave hybrid Large Neighborhood Search algorithm is developed and validated through a Hema Fresh case study in Xuzhou. Results define a clear “economic advantage zone” for drone adoption and reveal an adaptive assignment strategy: drones serve as mass-delivery tools in low-cost scenarios but act as “surgical tools” to prune inefficient truck segments in high-cost environments. These findings confirm that air–ground collaboration fosters a more resilient urban distribution system by balancing operational costs with environmental and social sustainability goals.