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出 处:《系统工程理论与实践》2012年第1期173-181,共9页Systems Engineering-Theory & Practice
基 金:国家自然科学基金(70771007);教育部新世纪优秀人才支持计划(NCET-05-0097)
摘 要:物流配送客户聚类问题是物流配送研究领域的基本问题,实际问题要求考虑客户分布的地理特性、客户配送量(即需求量)及配送车辆负载量等因素.针对配送中心未知的客户聚类问题,提出了一种考虑配送路网结构和配送量约束的聚类算法.利用所提出的"最短主干道距离",克服传统欧式距离不考虑配送道路信息的缺陷,在此基础上,利用约束聚类思想对传统k中心划分聚类算法CLARANS进行改进,使其在考虑地理信息的同时,能满足客户配送量和车辆负载量约束.最后对提出的算法进行了数值实验.Customer clustering is a fundamental issue in the logistics distribution study. Considering that retailers are bounded on certain geographic location and the clustering process is highly influenced by the distribution scale (that is the goods scale the customer demand) and loading scale of vehicles, we proposed a customer clustering method constrained by road network and distribution scale, for the clustering process with the location of distribution center unknown. The concept of "shortest arterial road distance (SARD)" takes distribution road information into account, which is ignored by the traditional Euclidean distance. Furthermore, a constrained clustering method for customers based on improved CLARANS was given, which consider the geographic information and fulfill the constraints of distribution scale. Finally, numeric experiments on both synthetic and real datasets justify the effectiveness of the method.
分 类 号:TP301.6[自动化与计算机技术—计算机系统结构]
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