基于改进蚁群和免疫算法融合的多配送中心路径优化  

Multi-Distribution Center Path Optimization Based on Improved Ant Colony and Immune Algorithm Fusion

作　　者：李进龙[1,2] 刘红星[3] 谢文杰 罗霞[1,2] 

机构地区：[1]西南交通大学,交通运输与物流学院,成都610031 [2]西南交通大学,综合交通运输智能化国家地方联合工程实验室,成都610031 [3]西南交通大学,电气工程学院,成都610031

出　　处：《交通运输工程与信息学报》2017年第4期87-94,共8页Journal of Transportation Engineering and Information

基　　金：中央高校基本科研业务费专项资金(SWJTUA0920502051307-03);高等学校博士学科点专项科研基金(P05413113613002);国家级大学生创新创业训练计划(201710613047)

摘　　要：随着物流行业的快速发展,货物运输需求和仓储需求也在不断增加。在构建物流网络的同时,需考虑车辆路径的配送中心选址问题,而现实中这两个问题是互相影响的。因此,本文建立了以免疫算法为框架,以蚁群算法为核心的综合算法模型。模型第一阶段改进了蚁群算法的禁忌搜索,并融合免疫算法;第二阶段设计了免疫-蚁群算法来求解车辆路径和配送中心选址的相互影响关系,并结合算例数据给出全局最优成本。算例结果表明,该综合算法模型明显优于传统免疫选址-蚁群寻优算法,可节约49.5%的总成本,验证了算法的可行性和有效性。With the rapid development of logistics industry, the demand for freight transportation and warehousing is also increasing. During the design phase of a logistics network, the vehicle routing problem and distribution center location problems are inter-related and need to be considered at the same time. This paper develops an integrated meta-heuristic-based framework to simultaneously solve these two problems. In the first stage, the taboo search of ant colony algorithm is improved and the immune algorithm is integrated; at the second stage, the immune-ant colony algorithm is designed to capture the inter-relationship between the vehicle routing problem and distribution center location problem. A numerical example is then provided to verify the proposed method. The results show that compared with the traditional immune location-ant colony optimization algorithm, the overall method can save 49.5% of the total cost.

关 键 词：物流工程 路径优化 算法融合 配送中心 蚁群算法 免疫算法 

分 类 号：U492.312[交通运输工程—交通运输规划与管理]