封臣算法:针对B2C电商物流配送中心的储位优化算法  

VASSAL ALGORITHM:OPTIMIZATION ALGORITHM FOR STORAGE LOCATION OF B2C E-COMMERCE LOGISTICS DISTRIBUTION CENTER

作　　者：刘威[1] 林海[1] 罗嫚玲 Liu Wei;Lin Hai;Luo Manling(Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan 430000,Hubei,China)

机构地区：[1]武汉大学国家网络安全学院空天信息安全与可信计算教育部重点实验室,湖北武汉430000

出　　处：《计算机应用与软件》2024年第5期203-211,303,共10页Computer Applications and Software

基　　金：国家自然科学基金面上项目(62072344)。

摘　　要：针对B2C电商配送中心人到货拣选系统中拣选作业时耗过高的问题,在考虑一品多位操作和商品关联度的基础上,提出新的储位优化算法——封臣算法。基于商品的关联度,商品间的关系首先被抽象成图结构。封臣算法将图中的节点视为单块领土,通过入侵流程确定各领土的封臣与领主,并基于封臣与领主,每个节点被分到至多两个分区中;调整节点个数至所需节点数;以社区为单位,按照贪心分配策略完成储位分配。实验结果表明,在商品种类数增多的情况下,封臣算法生成的储位分配方案相较随机策略优化程度有所提升;迭代400轮下该算法优于遗传算法、模拟退火算法、人工鱼群算法、粒子群算法3.00%、28.76%、22.03%、11.42%,且运行时间仅占到其0.04%~3.85%。Aiming at the problem of high consumption during picking operations in the B2C ecommerce distribution center's human arrival picking system,this paper proposes a new optimization algorithm for storage location—vassal algorithm by considering the multilocation operation of a single product and the degree of product correlation.The relationship between goods was modeled as a graph based on commodity correlation.The vassal algorithm considered each node of the graph as a single piece of territory.By determining the vassal and Lord of each territory through invasion,each node in the graph was divided into at most two communities based on the vassal and Lord.The algorithm adjusted the number of nodes to the desired number of nodes.We assigned the goods to storage shelf according to the generated communities by using greedy allocation strategy.The experimental results show that when the number of commodity types increases,the optimization degree of the storage allocation scheme generated by the vassal algorithm is improved compared with that of the random strategy.With iteration of 400 rounds,this algorithm is superior to genetic algorithm,simulated annealing algorithm,artificial fish swarm optimization and particle swarm optimization by 3.00%,28.76%,22.03%and 11.42%,and its running time only takes 0.04%~3.85%of other algorithms.

关 键 词：B2C电商 储位分配 封臣算法 社区划分 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]