D-CFPR: D numbers extended consistent fuzzy preference relations

TL;DR

This paper introduces D-CFPR, an extension of the consistent fuzzy preference relation that incorporates D numbers to better handle uncertain and incomplete information in decision-making.

Contribution

It proposes D-CFPR, extending CFPR with D numbers, enabling it to manage uncertainty and incomplete data, and integrates with D-AHP for improved MCDM solutions.

Findings

D-CFPR can handle uncertain and incomplete preferences.

It reduces to classical CFPR when uncertainty is absent.

The model integrates with D-AHP for systematic decision making.

Abstract

How to express an expert's or a decision maker's preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies additive transitivity property. However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The D-CFPR extends the classical CFPR by using a new model of expressing uncertain information called D numbers. The D-CFPR inherits the merits of classical CFPR and can be totally reduced to the classical CFPR. This study can be integrated into our previous study about D-AHP (D numbers extended AHP) model to provide a systematic solution for multi-criteria decision making (MCDM).