Aggregation operators constitute the core mathematical mechanism of multi-criteria decision-making by integrating multiple attribute evaluations into a single representative assessment. However, in intuitionistic fuzzy environments characterized by uncertainty, vagueness, and human hesitation, many existing aggregation operators inadequately capture nonlinear interactions among criteria and often weaken the influence of hesitancy information during aggregation. This paper examines the role of Yager-based aggregation mechanisms in preserving the structural integrity of intuitionistic fuzzy information and improving decision reliability under ambiguous conditions. A comprehensive family of Yager-based intuitionistic fuzzy aggregation operators is developed for multi-attribute decision-making problems, including weighted, ordered weighted, hybrid weighted averaging, and corresponding geometric operators. The principal advantage of the proposed operators lies in their flexible control of conjunction and disjunction behaviour through Yager’s t-norm and t-conorm, which enables a more balanced treatment of truth, indeterminacy, and falsity degrees compared with existing approaches. The system achieves logical stability through its core axiomatic properties which include idempotency and monotonicity and boundedness and commutativity. The framework shows stable ranking results which produce understandable outputs that identify differences between options during the selection process of eco-friendly companies in green supply chain management. The results show that Yager-based intuitionistic fuzzy aggregation creates a strong decision-support system which helps users make sustainable choices in complicated industrial ecosystems.
Kumar et al. (Fri,) studied this question.
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