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Customer characteristics are neglected in traditional VRPs in the past due to the heterogeneity and ambiguousness. This study presents a vehicle route optimization model in consideration of customer characteristics with three major components: (1) A hierarchical analysis structure is developed to convert customers\u2019 characteristics into linguistic variables, and fuzzy integration method is used to map the sub-criteria into higher hierarchical criteria based on the trapezoidal fuzzy numbers; (2) A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is proposed to group the customers into multiple clusters; (3) The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach is integrated into the dynamic programming approach to optimize vehicle routes in each cluster. A numerical case study in Anshun, China demonstrates the advantages of the proposed method by comparing with the other two prevailing algorithms. In addition, a sensitivity analysis is conducted to capture the impacts of various evaluation criteria weights. The results indicate our approach performs very well to identify similar customer groups and incorporate individual customer\u2019s service priority into VRP.<\/jats:p>","DOI":"10.3233\/ifs-151578","type":"journal-article","created":{"date-parts":[[2015,11,3]],"date-time":"2015-11-03T11:20:19Z","timestamp":1446549619000},"page":"1427-1442","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":26,"title":["Vehicle routing problem based on a fuzzy customer clustering approach for logistics network optimization"],"prefix":"10.1177","volume":"29","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Management, Chongqing Jiaotong University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolei","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beihang University, Beijing, 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