多仓库下物流无人机协同配送方法

无人机以其快捷、低成本优势,在物流配送中可以实现高效的包裹配送,但也存在着运行时间短、载重不足等缺点。针对当前配送建模考虑因素不够全面的问题,构建了基于能耗变化、混合时间窗和同时取送货的多仓库物流无人机配送模型,以实现配送经济成本最低。与经典的多基地车辆路径问题相比,文中研究的问题没有限制无人机出发和返回的仓库,旨在最大限度地减少无人机的数量和所有无人机行驶的总距离。为进一步优化物流无人机配送成本,针对遗传算法(GeneticAlgorithm,GA)寻优能力较差的问题,引入大规模领域搜索算法(LargeNeighbor-hoodSearchAlgorithm,LNS)作为局部搜索算子,进而提出基于改进 GA(ImprovedGA,IGA)的物流无人机协同配送算法。经仿真测试以及 Solomn标准数据验证,该算法较传统 GA在降低配送成本方面成效明显。

Collaborative Distribution Method of Logistics UAVs Under Multiple Warehouses

UAVs can achieve efficient parcel delivery in logistics distribution with their fast and low-cost advantages, but they also have the disadvantages of short operation time and insufficient load capacity. Aiming at the problem that the cur-rent distribution modeling considerations are not comprehensive enough, a multi- warehouse logistics UAV distribution model with energy consumption variation, mixed time windows and simultaneous pickup and delivery is constructed to achieve the lowest economic cost of distribution. Compared with the classical multi-base vehicle path problem, the prob-lem studied in this paper does not restrict the warehouses from which UAVs depart and return, and aims to minimize the number of UAVs and the total distance traveled by all UAVs. In order to further optimize the logistics UAV delivery cost,for the problem that the Genetic Algorithm(GA) is poor in finding the optimal capability, Large Neighborhood Search Al-gorithm(LNS) is introduced as the local search operator, and then the Improved GA(IGA) based on GA(IGA) based logis-tics UAV cooperative distribution algorithm. The proposed algorithm is validated by simulation tests and Solomn standard data, and it has a significant effect in reducing the distribution cost compared with the traditional GA.