Electric Vehicle Routing Problem with Drones Considering Weather Conditions and Time Windows

Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems.