电动物流车由于电池容量有限、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的有效推广。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,避免了初始种群的随机性和盲目性,同时,聚类簇内配送点的规模降低了非支配排序算法的运行时间和复杂度,并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性。

Electric logistics vehicles restrict their effective promotion in the field of logistics and distribution due to the limited battery capacity, long charging time and inadequate supporting facilities. To this end, based on clustering non-dominant sorting algorithm (AP-NSGA-II) to solve the problem of multi-target path optimization of electric logistics vehicles, a charging strategy is established, through the design of weighted AP clustering distribution clusters, to avoid the randomness and blindness of the initial population, at the same time, the size of distribution points within clusters reduces the running time and complexity of non-dominant sorting algorithms, and according to the distribution and distance relationship of charging stations, electric logistics vehicles to carry out part of the charging strategy. Finally, the effectiveness of the algorithm is proved by simulation experiments, and the differences between the full charge and partial charging conditions of electric logistics vehicles are compared.

TP 39

国家电网公司科技项目资助(1400-202057220A-0-0-00)