Enhanced Multi-Attribute Group Decision-Making under Uncertainty: A Novel Interval-Valued Fuzzy Linguistic Approach

Abstract

Handling uncertainty and hesitancy is a fundamental challenge in multi-attribute group decision-making (MAGDM). To address this, we introduce Interval-Valued Probabilistic Uncertain Linguistic Pythagorean Fuzzy Sets (IVPULPFSs), a new decision model that integrates interval-valued probabilistic representations with uncertain linguistic evaluations. This study proposes two novel MAGDM approaches: (1) an aggregation-based ranking method, and (2) a TODIM-based decision model incorporating psychological behaviours of decision-makers. To validate the effectiveness of these methods, we apply them to real-world decision-making problems, including human resource selection and doctoral thesis evaluation. The results demonstrate that IVPULPFS-based methods outperform existing fuzzy decision models by providing greater accuracy, flexibility, and robustness in handling uncertainty. This study offers a scalable decision-support framework for applications in finance, risk assessment, supply chain management, and intelligent transportation systems.