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Open AccessArticle

Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure

1
PRESAD Research Group, IMUVA, Departamento de Economía Aplicada, Universidad de Valladolid, 47011 Valladolid, Spain
2
CeBER, Faculty of Economics, University of Coimbra, 3004-512 Coimbra, Portugal
3
PRESAD Research Group, BORDA Research Unit, IMUVA, Departamento de Economía Aplicada, Universidad de Valladolid, 47011 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(3), 458; https://doi.org/10.3390/math8030458
Received: 17 February 2020 / Revised: 16 March 2020 / Accepted: 18 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Group Decision Making Based on Artificial Intelligence)
Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions. View Full-Text
Keywords: multiple criteria analysis; qualitative scales; ordinal proximity measures; stochastic analysis multiple criteria analysis; qualitative scales; ordinal proximity measures; stochastic analysis
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MDPI and ACS Style

González del Pozo, R.; Dias, L.C.; García-Lapresta, J.L. Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure. Mathematics 2020, 8, 458.

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