A New Probabilistic Linguistic Q-Rung Orthopair Fuzzy TODIM-MABAC Method Based on Correlation Coefficient

Probabilistic linguistic q-rung orthopair fuzzy set (PLq-ROFS) provides a flexible mathematical framework for dealing with complex decision-making problems where linguistic uncertainty and cognitive ambiguity coexist. Despite the growing sophistication of the model in terms of theoretical construction, its synthesis approach in multi-attribute group decision making (MAGDM) is still relatively understudied. This paper proposes a decision-making method that incorporates information entropy and the correlation coefficient. Firstly, a fuzzy entropy measure with axiomatic features is constructed to portray the ambiguity and uncertainty of fuzzy information based on the PLq-ROFS structure. Secondly, combining the entropy measure with the score function, a comprehensive correlation coefficient is proposed to quantify the correlation and similarity between fuzzy information units. Based on this, the correlation coefficient is embedded into the decision-making frameworks of TODIM and MABAC, and a hybrid decision-making method is proposed that combines psychological preference processing and boundary effect-assisted decision-making. The method considers both perception-driven and boundary identification through dominance calculation and proximity evaluation, and significantly improves the scheme differentiation and decision robustness. Finally, numerical examples verify the proposed model’s effectiveness and practicality.