Study on Supply Chain Partner Selection Based on Support Vector Machine


Article Preview

SVM is a novel machine learning technique developed on empirical risk minimization principle. SVM has many advantages in solving small sample size, nonlinear and high dimensional pattern recognition problem. Based on the study of SVM, this paper discusses its application in the supply chain partner selection that provides a reference for enterprise to select the partner.



Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen




W. B. Li, "Study on Supply Chain Partner Selection Based on Support Vector Machine", Applied Mechanics and Materials, Vols. 121-126, pp. 4779-4783, 2012

Online since:

October 2011





[1] Bowan, Edward H & Bruck Kogut. Redesigning the Firm. New York: Springer, 1995: 9-64.

[2] Rackham, N. etc. Getting Partnering Right. McGraw-Hill Inc., (1995).

[3] Beamon B M. Supply Chain Design and Analysis: Models and Methods. International Journal of Production Economics, 1998, 55: 281-294.

[4] Hendricks & Singhal. An Empirical Analysis of the Effect of Supply Chain Disruptions on long-run Stock Price Performance and Equity Risk of the Firm. Production and Operations Management 2005, 14(1): 35-52.


[5] Harland, C. Supply Chain Management: Relationships, Chains and Networks. British Journal of Management, 1996. 7: 63-80.

[6] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, (1995).

[7] Vapnik V N. Estimation of Dependencies Based on Empirical Data. Berli: Springer-Verlag, (1982).

[8] Burges C J C. A Tutorial on Support Vector Machines for Patten Recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-127.