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New Rank-Reversal Free Approach to Handle Interval Data in MCDA Problems

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12747))

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Abstract

In many real-life decision-making problems, decisions have to be based on partially incomplete of uncertain data. Since classical MCDA methods were created to be used with numerical data, they are often unable to process incomplete or uncertain data. There are several ways to handle uncertainty and incompleteness in the data, i.e., interval numbers, fuzzy numbers, and their generalizations. New methods are developed, and classical methods are modified to work with incomplete and uncertain data. In this paper, we propose an extension of the SPOTIS method, which is a new rank-reversal free MCDA method. Our extension allows for applying this method to decision problems with missing or uncertain data. The proposed approach is compared in two study cases with other MCDA methods: COMET and TOPSIS. Obtained rankings would be analyzed using rank correlation coefficients.

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Acknowledgments

The work was supported by the National Science Centre, Decision number UMO-2016/23/N/HS4/01931.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Department of Artificial Intelligence and Applied Mathematics, Research Team on Intelligent Decision Support Systems, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210, Szczecin, Poland

    Andrii Shekhovtsov, Bartłomiej Kizielewicz & Wojciech Sałabun

Authors
  1. Andrii Shekhovtsov
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  2. Bartłomiej Kizielewicz
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  3. Wojciech Sałabun
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Corresponding author

Correspondence to Wojciech Sałabun .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Shekhovtsov, A., Kizielewicz, B., Sałabun, W. (2021). New Rank-Reversal Free Approach to Handle Interval Data in MCDA Problems. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-77980-1_35

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