Advanced Decision-Making with Interval-Valued Probabilistic Linguistic Fuzzy Sets

Decision-making under uncertainty is a fundamental challenge in various fields, requiring methods that effectively capture probabilistic uncertainty and decision-maker hesitancy.This study introduces Interval-Valued Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy Sets (IVPULq-ROFSs), a novel framework that extends existing fuzzy set models by incorporating interval-valued probabilities and linguistic hesitancy.Based on this model, we develop two advanced Multi-Attribute Group Decision-Making (MAGDM) methods: (1) an aggregation-based approach, and (2) a TODIM-based decision method.To validate the effectiveness of the proposed methods, we apply them to autonomous vehicle sensor configuration selection, demonstrating enhanced decision robustness and reliability.The results indicate that IVPULq-ROFSs outperform conventional fuzzy models in handling complex and uncertain decision scenarios.Future research will explore algorithmic optimization, real-time applications, and integration with deep learning models for enhanced decision intelligence.