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Use of Possibility Measures for Ranking of Interval Valued Intuitionistic Fuzzy Numbers in Solving Multicriteria Decision Making Problems

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Book cover Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

In this paper, three possibility degree measures for comparing interval valued intuitionistic fuzzy numbers have been defined as extensions of existing possibility degree formulas in interval numbers and their equivalence are established and some basic properties are also proved. A simple mechanism proposed for solving MCDM problems by directly employing the possibility degree matrix generated from the proposed possibility degree measures. The introduced approach presents possibility degree as supplementary information to the ranking of alternatives in interval-valued intuitionistic fuzzy decision making. The validity and effectiveness of the developed methods are demonstrated through the comparative analysis and discussion of the three illustrative examples.

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Authors and Affiliations

  1. Department of Mathematics, Acharya Jagadish Chandra Bose College, Kolkata, 700020, India

    Samir Kumar

  2. Department of Mathematics, University of Kalyani, Kalyani, 741235, India

    Animesh Biswas

Authors
  1. Samir Kumar
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  2. Animesh Biswas
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Corresponding author

Correspondence to Animesh Biswas .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Kumar, S., Biswas, A. (2017). Use of Possibility Measures for Ranking of Interval Valued Intuitionistic Fuzzy Numbers in Solving Multicriteria Decision Making Problems. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_13

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  • DOI: https://doi.org/10.1007/978-981-10-6430-2_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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