为了提高城市环境下物流配送效率，以配送时间和配送成本为优化目标，建立“车辆 + 无人机”路径优化数学模型，提出一种基于聚类-Floyd-遗传算法的三阶算法。实验结果表明，该算法通过多阶数据处理，可有效降低运算量，克服了遗传算法收敛速度慢、易陷入局部最优的问题。对无人机容量进行灵敏度分析表明，无人机的配送能力随载重量增大而显著提升，载重量和最大航程同步提升能更好地发挥无人机的配送能力。和单纯车辆配送的方式相比，“车辆 + 无人机”配送模式总配送成本降低36.1%，总配送时间减少34.5%。证明了该算法在城市物流配送方面具有一定实用价值。

In order to improve the efficiency of logistics distribution in urban environment, taking delivery time and delivery cost as optimization objectives, a mathematical model of "vehicle-drone" route optimization was established, and a third-order algorithm based on clustering-Floyd-genetic algorithm was proposed. The experimental results show that the algorithm can effectively reduce the computational load through multi-order data processing, and overcome the slow convergence rate of genetic algorithm, easy to fall into the local optimal problem. The sensitivity analysis of drone capacity shows that the delivery capability of drone increases significantly with the increase of load, and the simultaneous increase of load and maximum range can bring the delivery capability of drone into full play. Compared with the vehicle-only mode, the total cost and time of "vehicle-drone" mode decrease by 36.1% and 34.5% respectively. It is proved that the algorithm has some practical value in city logistics distribution.