A novel framework for MCDM based on Z numbers and soft likelihood function
Yuanpeng He
TL;DR
This work introduces a novel soft likelihood function (SLF) framework for multi-criteria decision making under uncertainty, integrating intuitionistic fuzzy sets (IFS), Z-numbers, and information-theoretic measures. By combining the first-order information volume and credibility of expert judgments, the approach derives attitude characters that dynamically weight observations through an improved SLF built on OWA operators and Jensen–Shannon divergence. The method is applied to supplier evaluation across three rounds, demonstrating enhanced discrimination and consistent rankings even as data, participants, and suppliers change. Compared with traditional and existing SLF methods, the framework exhibits improved robustness to conflicting information and a clear, data-driven path from uncertainty to actionable decisions.
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.