Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure
| Autor: | José Luis García-Lapresta, Raquel González del Pozo, Luis C. Dias |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Proximity measure qualitative scales ordinal proximity measures Computer science Stochastic process General Mathematics lcsh:Mathematics stochastic analysis 02 engineering and technology Decision problem computer.software_genre lcsh:QA1-939 Multi criteria decision Multiple criteria analysis 020901 industrial engineering & automation Rule-based machine translation Robustness (computer science) Análisis de criterios múltiples 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) 020201 artificial intelligence & image processing Data mining Engineering (miscellaneous) computer |
| Zdroj: | UVaDOC. Repositorio Documental de la Universidad de Valladolid instname Mathematics Volume 8 Issue 3 Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Mathematics, Vol 8, Iss 3, p 458 (2020) |
| DOI: | 10.3390/math8030458 |
| Popis: | Producción Científica 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. Ministerio de Economía, Industria y Competitividad (Project ECO2016-77900-P) |
| Databáze: | OpenAIRE |
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