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Hierarchical clustering of interval-valued intuitionistic fuzzy relations and its application to elicit criteria weights in MCDM problems

Author

Listed:
  • Mamata Sahu

    (Delhi Technological University)

  • Anjana Gupta

    (Delhi Technological University)

  • Aparna Mehra

    (Indian Institute of Technology Delhi)

Abstract

The paper aims to apply the $$({\widetilde{\alpha }}, {\widetilde{\beta }})$$ ( α ~ , β ~ ) -cuts and the resolution form of the interval-valued intuitionistic fuzzy (IVIF ) relations to develop a procedure for constructing a hierarchical clustering for IVIF max–min similarity relations. The advantage of the proposed scheme is illustrated in determining the criteria weights in multi-criteria decision making (MCDM) problems involving IVIF numbers. The problem of finding the criteria weights is of critical interest in the domain of MCDM problems . A complete procedure is drawn to generate criteria weights starting from the criteria-alternative matrix of the MCDM problem with entries provided by a decision maker as IVIF numbers .

Suggested Citation

  • Mamata Sahu & Anjana Gupta & Aparna Mehra, 2017. "Hierarchical clustering of interval-valued intuitionistic fuzzy relations and its application to elicit criteria weights in MCDM problems," OPSEARCH, Springer;Operational Research Society of India, vol. 54(2), pages 388-416, June.
    • Handle: RePEc:spr:opsear:v:54:y:2017:i:2:d:10.1007_s12597-016-0282-5
    DOI: 10.1007/s12597-016-0282-5
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    References listed on IDEAS

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    1. Jalao, Eugene Rex & Wu, Teresa & Shunk, Dan, 2014. "An intelligent decomposition of pairwise comparison matrices for large-scale decisions," European Journal of Operational Research, Elsevier, vol. 238(1), pages 270-280.
    2. Lee, Hsuan-Shih, 1999. "Automatic clustering of business processes in business systems planning," European Journal of Operational Research, Elsevier, vol. 114(2), pages 354-362, April.
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    Cited by:

    1. Jiongmei Mo & Han-Liang Huang, 2019. "( T , S )-Based Single-Valued Neutrosophic Number Equivalence Matrix and Clustering Method," Mathematics, MDPI, vol. 7(1), pages 1-16, January.

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