The fuzzy sets and systems based on AFS.structure, EI algebra and EII algebra
Fuzzy Sets and Systems
Fuzzy group decision-making for facility location selection
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences—Informatics and Computer Science: An International Journal
Information Sciences—Informatics and Computer Science: An International Journal
Expert Systems with Applications: An International Journal
Classifying the segmentation of customer value via RFM model and RS theory
Expert Systems with Applications: An International Journal
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers
Expert Systems with Applications: An International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
A clustering method based on fuzzy equivalence relation for customer relationship management
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer segmentation of multiple category data in e-commerce using a soft-clustering approach
Electronic Commerce Research and Applications
Discontinuity of the trapezoidal fuzzy number-valued operators preserving core
Computers & Mathematics with Applications
Customer grouping for better resources allocation using GA based clustering technique
Expert Systems with Applications: An International Journal
Computers and Operations Research
The fuzzy clustering analysis based on AFS theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Large-scale pickup and delivery work area design
Computers and Operations Research
Bagged Clustering and its application to tourism market segmentation
Expert Systems with Applications: An International Journal
Review: Soft computing applications in customer segmentation: State-of-art review and critique
Expert Systems with Applications: An International Journal
A combined mining-based framework for predicting telecommunications customer payment behaviors
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.