A Supplier Selection Model Based on P-SVM with GA

Author

Xu, Sheng ; Xu, Yuan

Author_Institution

Sch. of Manage., Hefei Univ. of Technol., Hefei

fYear

2009

fDate

23-24 May 2009

Firstpage

1

Lastpage

3

Abstract

In this study, a potential support vector machines (P- SVM) with genetic algorithm (GA) is proposed to supplier selection. P-SVM is used to select optimal classifier using "support feature" by exchanging the roles of data points and features. On the other hand, one-against-one method is applied to solve the problem of multi-class. In addition, genetic algorithm is applied to accomplish the appropriate parameters selection so as to improve the performance of P-SVM as much as possible. The results of simulations show that the generalization performance of the methods based on P-SVM is higher than the ones based on standard SVM and can accomplish more scientific and reasonable criteria definition through training of P-SVM.

Keywords

customer services; genetic algorithms; pattern classification; purchasing; support vector machines; P-SVM; appropriate parameter selection; customer preference; genetic algorithm; optimal classifier selection; potential support vector machine; purchasing function; supplier selection model; Artificial intelligence; Decision making; Genetic algorithms; Globalization; Internet; Management training; Neural networks; Support vector machine classification; Support vector machines; Technology management;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-3893-8

Electronic_ISBN

978-1-4244-3894-5

Type

conf

DOI

10.1109/IWISA.2009.5072679

Filename

5072679