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Voting-KEmeny Median Indicator Ranks Accordance method for determining criteria priority and weights in solving multi-attribute decision-making problems

  • Soft computing in decision making and in modeling in economics
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Abstract

The KEmeny Median Indicator Ranks Accordance (KEMIRA) method is one of the newest multi-attribute decision-making (MADM) methods that is used for problems in which criteria are inherently divided into two or more categories. The main disadvantages of this method are the increasing computational complexity for large-scale problems as well as the inflexibility of the method with increasing number of criteria categories. Preferential voting is a decision-making method based on a linear programming model with weight restrictions, which in combination with KEMIRA can significantly eliminate its shortcomings. This paper presents the new Voting-KEMIRA method to achieve this purpose. First, the voting model is designed as a multiple objective decision-making (MODM) problem. Then, to reduce the computational complexity, the problem is reformulated as a linear programming model. It is then solved with a goal programming approach. Finally, the new hybrid method has been implemented on a real-world problem of a hospital construction and compared to the previous method. A significant reduction of calculations in decision-making has been achieved. In the Voting-KEMIRA model, criteria weights can be calculated more accurately and it also allows for use of numerous experts in determining the weights of the criteria.

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Authors and Affiliations

  1. Department of Mathematics, Semnan Branch, Islamic Azad University, Semnan, Iran

    Mehdi Soltanifar

  2. Department of Mathematical Modelling, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Aleksandras Krylovas & Natalja Kosareva

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  1. Mehdi Soltanifar
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  2. Aleksandras Krylovas
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Correspondence to Mehdi Soltanifar.

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Soltanifar, M., Krylovas, A. & Kosareva, N. Voting-KEmeny Median Indicator Ranks Accordance method for determining criteria priority and weights in solving multi-attribute decision-making problems. Soft Comput 27, 6613–6628 (2023). https://doi.org/10.1007/s00500-022-07807-0

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