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Different Approaches to Fuzzy Extension of an MCDA Method and Their Comparison

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Intelligent and Fuzzy Techniques: Smart and Innovative Solutions (INFUS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1197))

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Abstract

Fuzzy extension of an Multi-Criteria Decision Analysis (MCDA) method implies a choice of an approach to assessing corresponding functions of fuzzy variables and the use of a method for ranking of alternatives based on ranking of fuzzy quantities. In this paper, three key approaches to assessing functions of Fuzzy Numbers (FNs) are considered: approximate computations based on propagating triangular FNs through all computations, Standard Fuzzy Arithmetic (SFA), and Transformation Methods (TMs). In addition, three methods are used for ranking of FNs: Centroid Index, Integral of Means, and Yuan’s ranking method. Combination of an approach to assessing functions of FNs along with a fuzzy ranking method forms a Fuzzy MCDA (FMCDA) model. Distinctions in ranking alternatives by FMCDA models, which are different fuzzy extensions of an MCDA method, are considered for Fuzzy TOPSIS (FTOPSIS) models as an example. It is demonstrated with the use of Monte Carlo simulation that distinctions in ranking alternatives by different FTOPSIS models may be considered as significant. In such circumstances, the problem of choosing a model for application within multi-criteria decision analysis in fuzzy environment is relevant and requires further research.

Supported by the Russian National research project RFBR-19-07-01039.

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Authors and Affiliations

  1. Institute of Intelligent Cybernetic Systems, National Research Nuclear University MEPHI (IATE NRNU MEPHI), Obninsk-Moscow, Russian Federation

    Boris Yatsalo & Alexander Korobov

Authors
  1. Boris Yatsalo
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  2. Alexander Korobov
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Corresponding author

Correspondence to Boris Yatsalo .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  3. Department of Industrial Engineering, Istanbul Technical University, İstanbul, Turkey

    Basar Oztaysi

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

  5. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  6. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

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Yatsalo, B., Korobov, A. (2021). Different Approaches to Fuzzy Extension of an MCDA Method and Their Comparison. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_82

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