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Eliminating Rank Reversal Problem Using a New Multi-Attribute Model—The RAFSI Method

Author

Listed:
  • Mališa Žižović

    (Faculty of Technical Sciences in Čačak, University of Kragujevac, Svetog Save 65, 32102 Čačak, Serbia)

  • Dragan Pamučar

    (Department of Logistics, Military academy, University of Defence in Belgrade, Pavla Jurišića Šturma 33, 11000 Belgrade, Serbia)

  • Miloljub Albijanić

    (Faculty of Economics, Finance and Administration, Metropolitan University, 11000 Belgrade, Serbia)

  • Prasenjit Chatterjee

    (Department of Mechanical Engineering, MCKV Institute of Engineering, West Bengal, Howrah 711204, India)

  • Ivan Pribićević

    (Simplify Outsourcing d.o.o. Belgrade, 11000 Belgrade, Serbia)

Abstract

Multi-attribute decision-making (MADM) methods represent reliable ways to solve real-world problems for various applications by providing rational and logical solutions. In reaching such a goal, it is expected that MADM methods would eliminate inconsistencies like rank reversal issues in a given solution. In this paper, an endeavor is taken to put forward a new MADM method, called RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval), which successfully eliminates the rank reversal problem. The developed RAFSI method has three major advantages that recommend it for further use: (i) its simple algorithm helps in solving complex real-world problems, (ii) RAFSI method has a new approach for data normalization, which transfers data from the starting decision-making matrix into any interval, suitable for making rational decisions, (iii) mathematical formulation of RAFSI method eliminates the rank reversal problem, which is one of the most significant shortcomings of existing MADM methods. A real-time case study that shows the advantages of RAFSI method is presented. Additional comprehensive analysis, including a comparison with other three traditional MADM methods that use different ways for data normalization and testing the resistance of RAFSI method and other MADM methods to rank the reversal problem, is also carried out.

Suggested Citation

  • Mališa Žižović & Dragan Pamučar & Miloljub Albijanić & Prasenjit Chatterjee & Ivan Pribićević, 2020. "Eliminating Rank Reversal Problem Using a New Multi-Attribute Model—The RAFSI Method," Mathematics, MDPI, vol. 8(6), pages 1-16, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:1015-:d:374468
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    References listed on IDEAS

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    1. Huiyun Lu & Shaojun Jiang & Wenyan Song & Xinguo Ming, 2018. "A Rough Multi-Criteria Decision-Making Approach for Sustainable Supplier Selection under Vague Environment," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    2. P Leskinen & J Kangas, 2005. "Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 855-861, July.
    3. Mehdi KESHAVARZ GHORABAEE & Edmundas Kazimieras ZAVADSKAS & Zenonas TURSKIS & Jurgita ANTUCHEVICIENE, 2016. "A New Combinative Distance-Based Assessment(Codas) Method For Multi-Criteria Decision-Making," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 25-44.
    4. Xiongyong Zhou & Zhiduan Xu, 2018. "An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation," Sustainability, MDPI, vol. 10(7), pages 1-49, July.
    5. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
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    4. Deveci, Muhammet & Pamucar, Dragan & Oguz, Elif, 2022. "Floating photovoltaic site selection using fuzzy rough numbers based LAAW and RAFSI model," Applied Energy, Elsevier, vol. 324(C).

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