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    Supplier Selection: A Hybrid Approach Using ELECTRE and Fuzzy Clustering

    Azadnia, Amir and Ghadimi, Pezhman and Mat Saman, Muhamad Zameri and Wong, Kuan Yew and Sharif, Safian (2011) Supplier Selection: A Hybrid Approach Using ELECTRE and Fuzzy Clustering. In: Communications in Computer and Information Science. Springer, pp. 663-676.

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    Vendor selection is a strategic issue in supply-chain management for any organization to identify the right supplier. Such selection in most cases is based on the analysis of some specific criteria. Most of the researches so far concentrate on multi-criteria decision making (MCDM) analysis. However, it incurs a huge computational complexity when a large number of suppliers are considered. So, data mining approaches would be required to convert raw data into useful information and knowledge. Hence, a new hybrid model of MCDM and data mining approaches was proposed in this research to address the supplier selection problem. In this paper, Fuzzy C-Means (FCM) clustering as a data mining model has been used to cluster suppliers into groups. Then, Elimination and Choice Expressing Reality (ELECTRE) method has been employed to rank the suppliers. The efficiency of this method was revealed by conducting a case study in an automotive industry.

    Item Type: Book Section
    Keywords: Supplier selection; Multiple Criteria Decision Making; ELECTRE; Fuzzy Analytical Hierarchy Process; Fuzzy C-Means clustering;
    Academic Unit: Faculty of Social Sciences > School of Business
    Item ID: 15917
    Identification Number:
    Depositing User: Amir Azadnia
    Date Deposited: 04 May 2022 11:14
    Publisher: Springer
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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