Joint Optimization of Collaborative Routes and Charging Infrastructure for Electric Trucks and Drones in Emergency Logistics

In sparsely populated and disaster-prone regions, delivering critical resources like medical and emergency supplies presents significant challenges. These scenarios require highly efficient logistics systems, such as integrating Electric Truck (ET) and Unmanned Aerial Vehicle (UAV) to enhance emergency logistics. This paper proposes a dynamic joint optimization model to address coordinated route planning and dual charging needs for ET and drones, maximizing delivery efficiency while adapting to real-time disruptions. The model minimizes overall investment, construction, and time penalty costs while optimizing service coverage and strategically locating delivery routes and charging stations under battery range constraints. A real case study in the San Francisco area evaluates the model's performance, comparing it to scenarios without drone charging stations, the proposed model reduces delay costs by 40.07% and improves the on-time delivery rate by 27.26%. Under adverse conditions including wind speed surges, traffic delays, mechanical or communication failures, demand surges, and battery degradation, the proposed model achieves on-time delivery rates ranging from 85.51% to 96.42%, significantly outperforming the baseline ’ s rates of 25.83% to 75.65%, demonstrating the model's ability to adapt to dynamic conditions and provide a robust solution for future emergency logistics systems.