A novel framework for MCDM based on Z numbers and soft likelihood function

TL;DR

This paper introduces a new framework for multi-criteria decision making (MCDM) that combines Z numbers and a soft likelihood function to improve evaluation accuracy under uncertainty.

Contribution

It proposes a novel framework integrating Z numbers and soft likelihood functions for better uncertainty handling in MCDM, enhancing evaluation reliability.

Findings

The framework effectively extracts valuable information from uncertain assessments.

Comparative analysis shows the proposed method outperforms existing approaches.

Application results confirm the framework's validity and superiority.

Abstract

The optimization on the structure of process of information management under uncertain environment has attracted lots of attention from researchers around the world. Nevertheless, how to obtain accurate and rational evaluation from assessments produced by experts is still an open problem. Specially, intuitionistic fuzzy set provides an effective solution in handling indeterminate information. And Yager proposes a novel method for fusion of probabilistic evidence to handle uncertain and conflicting information lately which is called soft likelihood function. This paper devises a novel framework of soft likelihood function based on information volume of fuzzy membership and credibility measure for extracting truly useful and valuable information from uncertainty. An application is provided to verify the validity and correctness of the proposed framework. Besides, the comparisons with other existing methods further demonstrate the superiority of the novel framework of soft likelihood function.