基于聚类非支配排序的电动物流车路径规划及充电策略

电动物流车电池容量有限、充电时间长以及配套设施不健全等问题制约着其在物流配送领域中有效推广.为此,提出基于聚类非支配排序算法(AP-NSGA-Ⅱ)来解决电动物流车的多目标路径优化问题,建立了一种充电策略,通过设计加权AP聚类划分配送簇,避免初始种群的随机性和盲目性,簇内配送点规模降低了非支配排序算法的运行时间和复杂度,根据充电站的分布和距离关系,电动物流车执行部分充电策略.最后,通过仿真实验证明该算法的有效性,比较了电动物流车满充和部分充电条件的差异.

Path planning and charging strategy for electric logistics vehicles with clustering non-dominated sorting

The limited battery capacity, long charging time and inadequate supporting facilities of electric logistics vehicles restrict their effective promotion in the logistics and distribution field. In this paper, an improved cluster non-dominated sorting genetic algorithm (AP-NSGA-II) is proposed to solve the multi-objective route optimization problem of electric logistics vehicles. A charging strategy is established:dividing the distribution clusters by designing weighted AP clusters to avoid the randomness and blindness of the initial population, and reducing the running time and complexity of the non-dominated sorting algorithm by the scale of distribution points within the clusters. According to the distribution and distance relationship of charging stations, the electric logistics vehicles execute partial charging strategy. Finally, the effectiveness of the algorithm is demonstrated by simulation experiments, and the differences between full charging and partial charging conditions for electric logistics vehicles are compared.