Integrated Scheduling of Electric Vehicles and Delivery Drones: A PSO-GA Co-Evolutionary Approach

This paper proposes a dual level programming model for the coordinated scheduling of heterogeneous drones and electric trucks to address the issues of limited drone endurance and load capacity in natural disaster emergency logistics. The upper model optimizes the electric truck path and charging pile layout by improving the particle swarm optimization algorithm (PSO), while the lower model uses an improved genetic algorithm (GA) to allocate drone tasks and flight paths, achieving maximum delivery efficiency. Experiments have shown that in complex scenarios with 20 demand points and 3 charging stations, the total delivery time of this model is reduced by 18.4% compared to traditional methods, and the coverage rate of demand points reaches 100%, verifying the robustness and efficiency of the algorithm in dynamic environments.