Support Vector Machine Approach for Partner Selection of Virtual Enterprises

Jie Wang, Weijun Zhong, Jun Zhang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


With the rapidly increasing competitiveness in global market, dynamic alliances and virtual enterprises are becoming essential components of the economy in order to meet the market requirements for quality, responsiveness, and customer satisfaction. Partner selection is a key stage in the formation of a successful virtual enterprise. The process can be considered as a multi-class classification problem. In this paper, The Support Vector Machine (SVM) technique is proposed to perform automated ranking of potential partners. Experimental results indicate that desirable outcome can be obtained by using the SVM method in partner selections. In comparison with other methods in the literatures, the SVM-based method is advantageous in terms of generalization performance and the fitness accuracy with a limited number of training datasets.

Original languageEnglish
Pages (from-to)1247-1253
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2004

Bibliographical note

Funding Information:
Partner selection is an unstructured and multi-criterion decision problem. Qualitative analysis methods are commonly used in many research works [3]. However, quantitative analysis methods for partner selection are still a challenge. Existing quantitative methods in the related literatures can be classified into several categories: mathematical programming models, weighting models, genetic algorithms, dynamic clustering, neural network and fuzzy sets. Talluri and Baker [4] proposed a two-phase mathematical programming approach for partner selection by designing a VE, in which the factors of cost, time and distance were considered. The weighting model includes the linear scoring model and analytic hierarchy process (AHP). The linear scoring model assigns weights and scores arbitrarily. In the AHP model, the *This work was supported by grant No. 70171025 of National Science Foundation of China and grant No. 02KJB630001 of Research Project Grant of JiangSu, China.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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