考虑疫情影响的卡车无人机协同配送路径优化

在新型冠状病毒感染的肺炎（新冠肺炎）疫情蔓延的背景下，提出利用“卡车-无人机”协同配送新模式对疫情相对严重的地区进行物流配送，进一步研究该模式对疫情影响下物流配送的价值。考虑无人机最大飞行时间、载重以及道路条件等因素，将客户分为只能由卡车服务的客户、只能由无人机服务的客户以及卡车与无人机均能提供服务的客户3类，以车辆总服务时间最小为目标，建立车辆与无人机协同为客户提供配送服务的数学模型。设计嵌入简单启发式算法的混合邻域搜索算法，通过不同规模算例运算时间及多次运算解的波动性验证算法的有效性；通过对TSP算法、邻域搜索算子的不同组合的分析，找出最优的组合，进而对无人机最大飞行时间、无人机飞行速度载重影响因子进行了灵敏度分析。研究结果表明：计算结果验证了算法的有效性；TSP算法质量直接影响邻域搜索操作得到解的质量，可以通过高效TSP算法设计寻找更好的配送方案；无人机续航能力越强，目标函数就越小，无人机飞行速度载重影响因子越大，导致配送所需的服务时间越多，可以通过选用续航能力强、飞行速度受载重影响小的无人机提升配送服务效率。研究成果可以为城市发生重大灾难或特殊情况时，城市物流配送系统中无人机的应用提供指导和参考价值。

Optimization of Truck-drone Collaborative Distribution Route Considering Impact of Epidemic

Against the backdrop of the epidemic spread of coronavirus disease 2019 (COVID-19), a new mode of “truck-drone” collaborative distribution to carry out logistical distribution in areas with relatively serious epidemic conditions is proposed, along with further study of the value of this mode of distribution. Considering factors such as the maximum flight time, load, and road conditions of the UAV, this paper divides customers into three categories: customers who can only be served by trucks (truck-only customers), customers who can only be served by UAVs (UAV-only customers), and customers who can be served by both trucks and UAVs (flexible customers). We established a mathematical model in which vehicles and UAVs cooperated to provide distribution services for customers, with the objective of minimizing the total service time of the vehicles. A hybrid neighborhood search algorithm embedded with a simple heuristic algorithm was designed. The effectiveness of the algorithm was verified using the computing time for examples of different scales and the volatility of various operational solutions. Through an analysis of the different combinations of TSP algorithm and neighborhood search operator, an optimal combination was found, and then a sensitivity analysis of the UAV maximum flight time and UAV flight speed and load factors was carried out. The effectiveness of the algorithm was verified by the calculation results. It was found that the quality of the TSP algorithm directly affects the quality of the solution obtained by the neighborhood search operation, and a good distribution scheme can be found by implementing an efficient algorithm. The better the endurance of the UAV, the smaller the objective function. The greater the influence of the UAV's flight speed and load, the more service time needed for distribution. The distribution service efficiency can be improved by selecting UAVs that have high endurance and a small impact on flight speed by load. The research results of this study provide guidance and reference values for the application of UAVs in urban logistics distribution systems during major disasters and special circumstances.