In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Artificial Intelligence
On Clustering Validation Techniques
Journal of Intelligent Information Systems
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Industrial Engineering - Supply chain management
An integrated model for supplier selection decisions in configuration changes
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Vendor selection in outsourcing
Computers and Operations Research
Variable selection in clustering for marketing segmentation using genetic algorithms
Expert Systems with Applications: An International Journal
A hybrid approach to supplier selection for the maintenance of a competitive supply chain
Expert Systems with Applications: An International Journal
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
A three-phase integrated model for product configuration change problems
Expert Systems with Applications: An International Journal
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
International Journal of Bio-Inspired Computation
A fuzzy rule-based approach for screening international distribution centres
Computers & Mathematics with Applications
An intelligent supplier evaluation, selection and development system
Applied Soft Computing
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To differentiate part suppliers effectively, this study proposed a hybrid approach based on K-means, simulated annealing algorithm (SA), convergence factor particle swarm optimization (CPSO), and the Taguchi method abbreviated as KSACPSO. After all parts suppliers are confirmed by the bill of material (BOM), supplier cluster analysis was conducted on characteristics of customers' demands, including product cost, product quality, and procurement time using the proposed approach. To prove the KSACPSO approach has good clustering performance, the case study of a notebook computer was adopted to carry out the clustering procedures on parts suppliers, and compare the differences between the proposed approach and other hybrid methods. The execution results were analyzed to prove that the efficiency of the suggested KSACPSO approach is superior to K-means, K-means simulated annealing (KSA), K-means genetic algorithm (KGA), K-means genetic simulated annealing (KGSA), and K-means convergence factor particle swarm optimization (KCPSO).