基于改进型遗传算法的多目标配送线路优化仿真  

Simulation of Multi-target Delivery Routes Optimization Basedon Improved Genetic Algorithm

作　　者：张杨阳 张革伕 贺娜 梁腾飞 ZHANG Yang-yang;ZHANG Ge-fu;HE Na;LIANG Teng-fei(School of Economic Management and Law,South China University,Hengyang 421001;School of Computer Science and Technology,South China University,Hengyang 421001,China)

机构地区：[1]南华大学经济管理与法学学院,湖南衡阳421001 [2]南华大学计算机科学与技术学院,湖南衡阳421001

出　　处：《物流工程与管理》2023年第10期33-37,3,共6页Logistics Engineering and Management

基　　金：大学生创新课题项目:叫驴子(编号:S202110555008X)。

摘　　要：优化配送路径对节约物流成本、提高服务水平具有重大意义。文中构建了一个物流中心和33个配送节点应用场景,以配送线路总长度最短为目标,提出了一个改进型多种群竞争遗传算法模型。通过随机聚类来形成比较稳定的初始代,使用“最优-最劣”“次优-次劣”的选择交叉繁殖策略以及引入基于邻接点的自我繁殖,可改善陷入局部过早收敛情况,多种群竞争模式能提高随机向优的命中率。基于Python的仿真实验表明,改进后的遗传算法结果更优、更可靠,稳定性更好,对于生产实践极具指导价值。Optimizing the delivery routes is of great significance for saving logandistics costs and improving the service levels.A logistics center and 33 distribution nodes application scenario were constructed,an improved multi group competitive genetic algorithm model was proposed with the goal of minimizing the total length of delivery routes.By randomly clustering to form a relatively stable initial generation,using the“optimal vs.worst”and“sub-optimal vs.secondary inferiority”selection cross breeding strategy,and introducing self breeding based on adjacency points,the situation of falling into local premature convergence can be improved.Multiple group competition modes can improve the hit rate of random optimization.The simulation experiments based on Python show that the improved genetic algorithm results are better,more reliable and more stable,which has great guiding value for production practice.

关 键 词：遗传算法 旅行商问题 物流配送 

分 类 号：F224[经济管理—国民经济]