An SVM-based model for supplier selection using fuzzy and pairwise comparison

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.

[1]  Stephen F. Weber.  A Modified Analytic Hierarchy Process for Automated Manufacturing Decisions , 1993 .

[2]  Seyed Hassan Ghodsypour,et al.  A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming , 1998 .

[3]  C. P. Rao,et al.  Educational Buyers' Perceptions of Vendor Attributes , 1980 .

[4]  J. Adamson,et al.  Corporate Long‐range Planning Must Include Procurement , 1980 .

[5]  L. Ellram.  The Supplier Selection Decision in Strategic Partnerships , 1990 .

[6]  L. D. Boer,et al.  A review of methods supporting supplier selection , 2001 .

[7]  Donald W. Dobler,et al.  Purchasing and Materials Management: Text and Cases , 1977 .

[8]  Paul Melis.  Thinking with models: Mathematical models in the physical, biological and social sciences: Thomas L. SAATY and Joyce M. ALEXANDER Volume 2 in: International Series in Modern Applied Mathematics and Computer Science, Pergamon, Oxford, 1981, xi + 181 pages, £15.90 , 1982 .

[9]  W. C. Benton,et al.  Vendor selection criteria and methods , 1991 .

[10]  Decision of the mechanical drive type with fuzzy characters based on the support vector machine , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[11]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Thomas Serre,et al.  Hierarchical classification and feature reduction for fast face detection with support vector machines , 2003, Pattern Recognit..