带时间窗的电动汽车与无人机协同取送货路径问题OA

To optimize delivery routes for electric vehicles and drones operating within time windows,and to enhance the operational efficiency of the collaborative system,this paper built a mixed-integer programming model with the minimal total system costs.The model takes into consideration vehicle and drone range capabilities,payload limitations,matching delivery demands,and time window constraints.To address the computational complexity,an improved GA-ALNS algorithm based on 3D correlation disruption and dynamic energy consumption repair strategies was designed.It enhanced constraint handling capabilities through optimized removal and repair operators,while incorporating an elite pool and path reconnection strategy to deepen global optimization.Algorithm performance was validated through case studies.Results demonstrate the improved GA-ALNS algorithm exhibits superior solution quality and efficiency.Compared to LNS,ALNS,and HPSO algorithms,it has obvious advantages in medium-to-large-scale cases,with the average cost reductions reaching 8％,11％,and over 20％respectively.Sensitivity analysis confirms drone endurance and payload capacity are critical factors affecting the operation of the collaborative system.