摘要
为了解决自取货的物流末端单一节点选址问题,在服务顾客数量和地理位置已知的前提下,考虑最远取货距离的约束,建立以客户满意度模糊隶属度为目标函数的数学模型。为了求解该模型,运用均值聚类给出初始可行节点,以满意度较小的客户为顶点,在分析其合理性的基础上,设计基于三角形外心和等分点的启发式算法优化初始解,算法允许接受一次次优解以避免陷入局部最优,并通过记忆数组来跟踪搜索过程,最终输出过程最优解。最后给出算例分析,证明了所提出的图上寻优算法优于均值聚类和点密度聚类算法。
In order to solve the location problem of single logistics terminal node of picking up goods by self-help pattern, the constraint of farthest distance is considered under the premise that customers’ number and geographical location are given, and a mathematical model is established with the objective function of the fuzzy membership degree of customer satisfaction. For the purpose of solving the model, the means clustering is applied to obtain an initial feasible node, and then a heuristic algorithm is proposed to optimize the initial solution based on excentre of triangle and equal diversion point by taking customers of less satisfaction as vertexes, which allows to accept one-time suboptimum solution in order to avoid falling into local optimum. In addition, a memory array is set to ensure that final output is optimal solution throughout the process. Finally, a numerical example is given, which demonstrates that the heuristic algorithm is better than both the means clustering and the point density clustering.
作者
肖玉徽
楼振凯
XIAO Yuhui;LOU Zhenkai(College of Business Administration,Haikou University of Economics,Haikou 571127,China;School of Management & Economics,Beijing Institute of Technology,Beijing 100081,China)
出处
《工业工程》
北大核心
2019年第3期126-131,共6页
Industrial Engineering Journal
基金
海南省哲学社会科学规划资助项目(HNSK(YB)16-57)
海口经济学院校级重点科研资助项目(hjkz18-04)
关键词
最远取货距离
客户满意度
均值聚类
三角形外心
farthest distance of pick up goods
customer satisfaction
means clustering
excentre of triangle