• DocumentCode
    2145634
  • Title

    Supplier Selection Based on Rough Sets and Analytic Hierarchy Process

  • Author

    Wang, Lei ; Ye, Jun ; Li, Tianrui

  • Author_Institution
    Sch. of Inf. Sci. & Technol., Southwest Jiaotong Univ., Chengdu, China
  • fYear
    2010
  • fDate
    14-16 Aug. 2010
  • Firstpage
    787
  • Lastpage
    790
  • Abstract
    Supply Chain Management (SCM) is a kind of mode of management which characteristic is systematic, integrative and agile. The selection of supplier is one of the crucial steps in constructing a supply chain system. In this paper, an improved analytic hierarchy process method based on the concept of attribute significance in rough set theory is adopted in the supplier selection of SCM. Firstly, an optimization model which can determine the combinatorial judgement matrix is established. On the basis of this optimization model, a combinatorial judgement matrix is constructed by combining the subjective judgment matrix derived from the AHP method and the objective judgment matrix derived from the attribute significance in rough set theory, Then an initial probing is accomplished on the selection of supplier in SCM by the aforesaid comprehensive judgement matrix. Finally, an example is demonstrated to verify the validity and feasibility of the selection of supplier in SCM based on the proposed method.
  • Keywords
    combinatorial mathematics; decision making; matrix algebra; optimisation; rough set theory; supply chain management; AHP method; SCM; analytic hierarchy process; attribute significance concept; combinatorial judgement matrix; optimization model; rough set theory; subjective judgment matrix; supplier selection; supply chain management; Decision making; Indexes; Set theory; Supply chains; Analytic Hierarchy Process; Decision making; Rough Set; Selection of Supplier; Supply Chain Management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing (GrC), 2010 IEEE International Conference on
  • Conference_Location
    San Jose, CA
  • Print_ISBN
    978-1-4244-7964-1
  • Type

    conf

  • DOI
    10.1109/GrC.2010.75
  • Filename
    5576078