An enhanced QUALIFLEX decision-making framework incorporating power-form scoring mechanisms within a circular intuitionistic fuzzy paradigm

This study introduces an enhanced QUALItative FLEXible (QUALIFLEX) decision-making framework that integrates power-form scoring mechanisms within a Circular Intuitionistic Fuzzy (CIF) paradigm. A principal innovation of this study is the formulation of parameter-driven, natural exponential-based power-form CIF scoring mechanisms that extend beyond the limitations of conventional linear aggregation models. By capturing non-linearity and interdependencies among CIF parameters, this approach enhances the reliability and robustness of decision evaluations. Additionally, this study formulates a permutation-based ranking mechanism tailored to CIF properties, reinforcing theoretical consistency while improving computational efficiency. The proposed framework integrates CIF membership, non-membership, and circular radius components into the QUALIFLEX methodology, thereby facilitating a finer-grained appraisal of alternatives under conditions of uncertainty. Furthermore, this research advances QUALIFLEX by incorporating CIF principles, refining preference modeling to enable a more comprehensive assessment of alternatives. To establish a preferential ranking, concordance–discordance metrics are applied to each dyadic comparison within the predefined preorder structure, followed by an evaluation of the overall metric across all permutations. The most suitable ranking is subsequently derived by identifying the permutation that maximizes the concordance–discordance measurement, ensuring a logically sound and robust decision outcome. Additionally, this study formulates a structured algorithmic procedure for the CIF-based QUALIFLEX methodology, ensuring systematic implementation from problem definition to optimal ranking derivation. Beyond its theoretical contributions, the present investigation probes the practical utility of CIF-QUALIFLEX within evolving decision-making arenas, particularly in assessing Artificial Intelligence (AI)-driven Clinical Decision Support System (AI-CDSS) providers. By incorporating power-form CIF scoring mechanisms into real-world scenarios, this research fosters a more resilient and adaptable QUALIFLEX-oriented decision analysis framework. Overall, this study advances CIF-based decision analytics by introducing a novel CIF-QUALIFLEX methodology that improves computational modeling, enhances ranking accuracy, and strengthens decision-making under complex uncertainty. The integration of power-form scoring mechanisms establishes a robust foundation for future developments in permutation-driven decision analysis, with significant implications for both theoretical research and practical applications.