A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization

被引:109
作者
Wang, Yong [1 ]
Ma, Xiaolei [2 ]
Lao, Yunteng [2 ]
Wang, Yinhai [2 ]
机构
[1] Chongqing Jiaotong Univ, Sch Management, Chongqing 400074, Peoples R China
[2] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
基金
中国国家自然科学基金;
关键词
Customer clustering; Fuzzy set theory; Clustering algorithm; Clustering validity index; Axiomatic fuzzy set; GROUP DECISION-MAKING; SUPPLY CHAIN; AFS ALGEBRA; DESIGN; SEGMENTATION; MANAGEMENT; SELECTION; MODEL; ALGORITHM; OPERATORS;
D O I
10.1016/j.eswa.2013.07.078
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization. By properly grouping those customers with similar characteristics, logistics operators are able to reduce operational costs and improve customer satisfaction levels. However, due to the heterogeneity and high-dimension of customers' characteristics, the customer clustering problem has not been widely studied. This paper presents a fuzzy-based customer clustering algorithm with a hierarchical analysis structure to address this issue. Customers' characteristics are represented using linguistic variables under major and minor criteria, and then, fuzzy integration method is used to map the sub-criteria into the higher hierarchical criteria based on the trapezoidal fuzzy numbers. A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is developed to group the customers into multiple clusters. The clustering validity index is designed to evaluate the effectiveness of the proposed algorithm and find the optimal clustering solution. Results from a case study in Anshun, China reveal that the proposed approach outperforms the other three prevailing algorithms to resolve the customer clustering problem. The proposed approach also demonstrates its capability of capturing the similarity and distinguishing the difference among customers. The tentative clustered regions, determined by five decision makers in Anshun City, are used to evaluate the effectiveness of the proposed approach. The validation results indicate that the clustered results from the proposed method match the actual clustered regions from the real world well. The proposed algorithm can be readily implemented in practice to help the logistics operators reduce operational costs and improve customer satisfaction levels. In addition, the proposed algorithm is potential to apply in other research domains. Published by Elsevier Ltd.
引用
收藏
页码:521 / 534
页数:14
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