Title :
An SVM-based model for supplier selection using fuzzy and pairwise comparison
Author :
Sun, Hua-li ; Xie, Jian-Ying ; Xue, Yao-feng
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ., China
Abstract :
Supplier selection is a multi-criteria problem including various factors. In order to select the best suppliers, it is necessary to make a trade off among these factors when some of them conflict, where few existing systems work well. In this paper a supplier selection model based on support vector machine (SVM) is firstly developed. The supplier selection criteria and quantitative methods using fuzzy and pairwise comparison are presented. Simulations show that the proposed supplier selection model is a more useful additional tool than fuzzy synthetical evaluation for supplier management.
Keywords :
fuzzy set theory; learning (artificial intelligence); supply chain management; support vector machines; SVM-based model; fuzzy synthetical evaluation; multicriteria problem; pairwise comparison; quantitative method; supplier management; supplier selection model; support vector machine; Artificial neural networks; Automation; Electronic mail; Fuzzy neural networks; Information technology; Machine learning; Neural networks; Risk management; Sun; Support vector machines; SVM; Supplier selection; fuzzy synthetical evaluation; neural network; pairwise comparison;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527571