Enhanced Multi-Attribute Ideal-Real comparative analysis with the circular intuitionistic fuzzy framework: Application to hybrid cloud services

This paper underscores the utilization of the Circular Intuitionistic Fuzzy (CIF) framework to enhance the Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodology, emphasizing its practical relevance through an application to hybrid cloud services. The CIF framework incorporates membership and non-membership components accompanied by a radius, forming a deformable circular structure within an intuitionistic fuzzy interpretation triangle. The study utilizes geometric mean techniques to maintain consistency in CIF evaluative ratings and importance levels while reducing the impact of outliers. By incorporating upper and lower importance levels and parameterized CIF scoring functions, the methodology ensures balanced weight determination. Refined radius operations further enhance CIF data analysis, improving the methodology's comprehensiveness. The enhanced CIF MAIRCA approach balances theoretical and real-world evaluations, harmonizes criteria, and computes aggregate disadvantage gap measures to rank alternatives, with smaller gaps indicating better options. This research illustrates the real-world effectiveness of the developed methodology through a hybrid cloud services case study. By exploring various parameter configurations, it highlights the approach's robustness, adaptability, and ability to ensure stability and reliability in complex real-world scenarios. To extend the utility of the enhanced CIF MAIRCA methodology to other decision-making scenarios, this study applies it to a vendor evaluation case. Comparative analyses with other models highlight its strengths in managing uncertainty, adaptability, and precision, affirming its value as a reliable decision-support tool.