Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self‐confidence and its application in group decision making
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Wiley
Materia
Additive consistency Fuzzy preference relations with self-confidence (FPRs-SC) Induced ordered weighted averaging (IOWA)
Date
2018-12-18Referencia bibliográfica
Liu, X.; Xu, Y.; Montes, R.; Dong, Y.; Herrera, F., Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self‐confidence and its application in group decision making. International Journal of Intelligent Systems, 34 (5), 920-946. (2019)
Sponsorship
Instituto Interuniversitario de Investigación en Data Science and Computational Intelligence (DaSCI)Abstract
El artículo analiza la utilización de relaciones de preferencia difusas con autoconfianza (FPRs-SC) en problemas de toma de decisiones en grupo (GDM). El artículo se centra en el análisis de la consistencia aditiva de las FPRs-SC y su aplicación en GDM. Introduce leyes operativas para FPRs-SC, propone un índice de consistencia aditiva que considera tanto los valores de preferencia difusos como la autoconfianza, y presenta un algoritmo iterativo para tratar la inconsistencia en FPRs-SC. El estudio esboza un proceso para calcular FPR-SC colectivos agregando los individuales mediante un operador basado en índices de autoconfianza, dando más peso a los expertos más autoconfiados. Además, se diseña una función de puntuación de la autoconfianza para determinar la mejor alternativa en GDM con FPRs-SC. La viabilidad y validez de la investigación se demuestran mediante un ejemplo ilustrativo y análisis comparativos. The article discusses the utilization of fuzzy preference relations with self-confidence (FPRs-SC) in group decision-making (GDM) problems. The paper focuses on analyzing the additive consistency of FPRs-SC and its application in GDM. It introduces operational laws for FPRs-SC, proposes an additive consistency index considering both fuzzy preference values and self-confidence, and presents an iterative algorithm to address inconsistency in FPRs-SC. The study outlines a process to compute collective FPR-SC by aggregating individual ones using a self-confidence indices-based operator, giving more weight to the most self-confident experts. Additionally, a self-confidence score function is designed to determine the best alternative in GDM with FPRs-SC. The research's feasibility and validity are demonstrated through an illustrative example and comparative analyses.