Optimized Last-Mile Delivery: Integrating Drones With Carrier Vehicles

This paper develops an optimized hybrid drone-carrier logistics framework tailored to realistic urban delivery scenarios. Initially, we derive the optimal drone departure and rendezvous points numerically to minimize the total drone travel distance in a simplified scenario that involves a single drone, a carrier vehicle, and a single customer. Subsequently, we extend the model to sequentially serve multiple customers. We introduce piecewise rectilinear carrier vehicle paths closely resembling realistic city block layouts. Drone battery constraints are then integrated into the model, requiring drones to evaluate their battery capacity before each delivery, leading to decisions on recharging or skipping deliveries if necessary. Finally, multiple drones are deployed simultaneously, with customer locations partitioned optimally using K-means clustering. Each drone independently serves its assigned cluster, optimizing delivery sequences through an initial nearest-neighbor heuristic followed by a 2-OPT route refinement. Simulations demonstrate significant reductions in travel distances, improved drone utilization, and substantial enhancements in operational efficiency, validating the scalability and real-world applicability of the proposed multi-drone, battery-constrained logistics system.