改进蚁群算法对多配送中心物流配送路径优化  

Optimization of Logistics Distribution Path in Multiple Distribution Centers by Improved Ant Colony Algorithm

作　　者：兰国辉[1] 张玉遇 LAN Guohui;ZHANG Yuyu(School of Economics and Management,Anhui University of Science and Technology,Huainan Anhui 233200,China)

机构地区：[1]安徽理工大学经济与管理学院,安徽淮南233200

出　　处：《长春工程学院学报（自然科学版）》2024年第2期119-124,共6页Journal of Changchun Institute of Technology：Natural Sciences Edition

基　　金：淮南市科技研发项目(2021A244);安徽省教学研究重点项目(2021jyxm0351)。

摘　　要：改进蚁群算法(IACO)是在传统蚁群算法(ACO)的基础上,解决带有软时间窗的路径优化问题(VRPSTW)。首先运用罚数法分割客户点,匹配配送中心寻找初始解,其次引入新的信息素更新公式,最后运用插入算子,倒转算子进行变邻域搜索,得出寻优序列。将两算法的过程差异与结果差异进行比较,结果表明:在多配送中心的前提下,对比传统算法,改进后的优势在于前期求解速度与结果求解能力得到提升,带有软时间窗的多配送中心能更好地兼顾成本与客户满意度,也更符合企业和用户对路径优化的实际需求。Improved Ant Colony Algorithm(IACO)is a solution to optimizing the vehicle routing problems with soft time windows(VRPSTW)based on traditional Ant Colony Algorithm(ACO).Firstly,the penalty method is used to segment customer points and match the distribution center to find the initial solution.Secondly,a new pheromone update formula is introduced.Finally,the insertion operator and inversion operator are used for variable neighborhood search to obtain the optimization sequence.Comparing the differences in the process and results of the two algorithms,the results show that the improved algorithm has the advantage of improving the early solving speed and result solving ability compared to traditional algorithms under the premise of multiple distribution centers.Multiple distribution centers with soft time windows can better consider costs and customer satisfaction,and also better meet the actual needs of enterprises and users for path optimization.

关 键 词：蚁群算法 罚数法 变邻域搜索 软时间窗 客户满意度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F252[自动化与计算机技术—控制科学与工程]