A novel framework for MCDM based on Z numbers and soft likelihood function
This work addresses uncertainty in expert-based decision-making for fields like information management, though it appears incremental as it builds on existing fuzzy set and soft likelihood methods.
The paper tackled the problem of obtaining accurate evaluations from expert assessments under uncertainty by proposing a novel framework based on Z numbers and soft likelihood functions, which was validated through an application and shown to be superior in comparisons with existing methods.
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.