面向物流车辆路径规划的改进粒子群优化算法  

Improved Particle Swarm Optimization Algorithm for Logistics Vehicle Routing Planning

作　　者：耿永忠 黄秋原 余太松 刘家鹏 GENG Yongzhong;HUANG Qiuyuan;YU Taisong;LIU Jiapeng(Sinopharm Group Shanghai Biopharmaceutical Co.,Ltd.,Shanghai 200000,China)

机构地区：[1]国药控股上海生物医药有限公司,上海200000

出　　处：《自动化与信息工程》2025年第2期25-31,62,63,共9页Automation & Information Engineering

摘　　要：为优化物流车辆的配送路径,提高配送效率,降低运营成本,提出一种面向物流车辆路径规划的改进粒子群优化(PSO)算法。该算法采用贪婪算法初始化解,并利用基于迭代过程的粒子交叉更新算法实现搜索范围和搜索速度的平衡,使求解方案的总路径长度更短。相较于遗传算法和经典的PSO算法,该算法规划的总路径长度、适应度值、计算耗时均更小。在实际规划任务中,该算法比人工调度规划更合理。To optimize the logistics vehicle routing planning,improve delivery efficiency,and reduce operational costs,an improved particle swarm optimization(PSO)algorithm for logistics vehicle routing planning is proposed.This algorithm employs a greedy algorithm to initialize the solutions and utilizes a particle crossover update algorithm based on the iterative process to balance the search scope and search speed,thereby achieving shorter total path lengths for the solutions.Compared to genetic algorithms and classical PSO algorithms,this algorithm exhibits smaller total path lengths,lower fitness values,and reduced computational time.In practical routing tasks,the algorithm generates more rational planning results than manual scheduling.

关 键 词：物流车辆路径规划 改进的粒子群优化算法 贪婪算法 粒子交叉更新算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]