考虑客户价值的卡车与无人机联合配送时变路径优化方法

针对拥堵情况日益严重导致的物流业配送时效不高、客户价值低等问题，综合考虑客户价值和成本等因素，提出了一种卡车与无人机联合配送时变路径的优化方法。考虑到配送过程中不同时段的拥堵情况，采用速度分布函数刻画车辆的行驶速度，同时考虑客户的时间窗、车辆的载重和无人机的载重等约束条件，建立了成本最小的数学模型。根据模型的特点，引入K-means对客户的位置进行聚类，设计混合的粒子群算法对模型进行求解。最后通过Solomom数据进行模拟仿真实验，对模型和算法的有效性进行验证。实验结果表明，与未考虑客户价值静态路网模型相比，该模型在降低9.32%成本的情况下，同时提高了16.83%的客户价值和21.28%的客户满意度，所提算法在降低配送成本和提高企业经济效益方面具有一定的有效性。

Research on time-varying route optimization method for truck and UAV joint delivery considering customer value

Aiming at the problems of low timelessness and low customer value caused by the increase of vehicle ownership and congestion, the paper proposed a time-varying route optimization method of truck and UAV joint delivery after considering the factors of customer value and cost. Considering the congestion in different time periods in the delivery process, it used the speed distribution function to describe the vehicle speed and the constraints such as customer time window and it toke vehicle load and UAV load into account to establish a mathematical model with minimum cost. According to the characteristics of the model, it introduced K-means to cluster customers'' locations and designed a hybrid particle swarm optimization algorithm to solve the model. Finally, the paper used Solomom data to conduct simulation experiments to verify the validity of the model and algorithm. The experimental results show that compared with static network model without considering customer value, the model can reduce 9.32% cost, improve 16.83% customer value and 21.28% customer satisfaction, and the proposed algorithm in this paper has certain effectiveness in reducing distribution cost and improving enterprise economic benefits.