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Fermatean fuzzy preference relations and their application

adaptive-and-self-organizing-systems
algorithms-and-analysis-of-algorithms
data-mining-and-machine-learning
operating-systems

Abstract

In this paper, we suggest a decision-making process utilizing Fermatean fuzzy preference relations, focusing on examining the consistency of Fermatean fuzzy preference relations. Firstly, we introduce two optimization models to minimize the deviation from additive consistency. The optimal deviation values obtained from the model outcomes make it possible for us to develop consistency in considering preference relations. Hence, mathematical programming models are built according to consistent collective preference relations. In this stage, we give a new score function that will enable ranking Fermatean fuzzy numbers. Then, we define various Fermatean fuzzy preference relations (consistent, incomplete, consistent-incomplete, and acceptable-incomplete), an additive consistency-based Fermatean fuzzy priority vector, and an inconsistency adjustment approach for Fermatean fuzzy preference relations. We propose a model to obtain incomplete decisions in incomplete Fermatian fuzzy preference relations.
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