A point operator-driven approach to decision-analytic modeling for multiple criteria evaluation problems involving uncertain information based on T-spherical fuzzy sets. (1st October 2022) - Chen, Ting-Yu
Highlights: Initiating a point operator-driven approach to decision-analytic modeling. Constructing T-spherical fuzzy point operators for upper and lower estimations. Proposing point operator-driven measures for attitudinal-based expected values. Exploring five real-world applications to show the effectuality and applicability. Comparative analysis about effectiveness, robustness, and parameter settings. Abstract: T-spherical fuzzy (T-SF) sets are generalized versions of renowned high-order fuzzy models that provide powerful tools for managing ambiguous and equivocal information in complex decision-making environments. This study concentrates on a novel point operator-driven approach to decision-analytic modeling for multiple criteria decision analyses (MCDAs) that pose substantial computational difficulty in T-SF uncertain information. This study explores two easily operated T-SF point operators to ascertain upper and lower estimations of T-SF uncertain information. Also, this study takes advantage of the notions of score functions and continuous ordered weighted average operators to launch an efficacious T-SF point operator-driven decision model for multiple criteria analysis and evaluation tasks. In particular, the mechanism of the assignation parameters and the basic unit-interval monotonic parameter can help decision-makers treat T-SF information with great proficiency and flexibility, which makes intricate MCDA processes more intelligent. In addition to five real-world applications, a comprehensive comparative analysis of the tests for effectiveness, robustness, and parameter settings are conducted to carefully validate the developed point operator-driven techniques, and the analytical results corroborate the effectuality and favorable features of the proposed methodology.