Expert Systems with Applications, Volume 41, Issue 2, 1 February 2014, Pages 521-534.
Yong Wang, Xiaolei Ma, Yunteng Lao, Yinhai Wang.
School of Management, Chongqing Jiaotong University, Chongqing 400074, China and
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195 2700, USA.
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.
Affiliations and contacts:
Yong Wang, School of Management, Chongqing Jiaotong University, Chongqing 400074, China, [email protected]
Xiaolei Ma, School of Transportation Science and Engineering, Beihang University, Beijing 100191, China, [email protected]
Yunteng Lao, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA, [email protected]
Yinhai Wang, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA, [email protected]