A circular intuitionistic fuzzy assignment model with a parameterized scoring rule for multiple criteria assessment methodology

Abstract

The notion of circular intuitionistic fuzzy (C-IF) sets, which utilizes a malleable circle to depict uncertainties and encompasses membership and non-membership constituents at its core, constitutes a progressive advancement of standard intuitionistic fuzzy sets. This paper concerns the utilization of a C-IF assignment model along with a parameterized scoring rule for a methodology involving multiple criteria assessment. This study introduces a novel parameterized C-IF scoring function, addressing limitations in current scoring methodologies. The newly proposed scoring function incorporates datum and allocating parameters, offering enhanced adaptability and practicality. Unlike some existing C-IF scoring functions, this parameterized function effectively accounts for the radius of uncertainty, ensuring more accurate and reliable C-IF number comparisons. Comparative assessments with other scoring techniques demonstrate the superiority and stability of proposed function across various C-IF datasets. This newly introduced scoring function proves valuable in addressing multiple criteria assessment challenges within C-IF contexts, providing decision analysts with a dependable tool for complex decision-making scenarios. This research delves into real-world instances, including supplier assessment and healthcare waste disposal, illustrating a practical implementation of the model. Additionally, a comparison study and investigation have been done to emphasize the benefits of the parameterized C-IF scoring procedure employed in the postulated C-IF assignment methodology. This study offers substantial contributions, encompassing: (i) devotion to the establishment of a parameterized C-IF scoring methodology, (ii) assurance of consistent and rational C-IF scoring outcomes through the proposed parameterized scoring rule, (iii) the methodology’s flexibility, allowing customization of parameters to align with decision-makers’ preferences, (iv) improvement in the stability of C-IF assignment modeling through the parameterized C-IF scoring framework, and (v) demonstrated its practical viability through real-world applications in tackling challenges associated with multiple criteria assessment in C-IF contexts.