This research article aims at establishing the foundations of generalized hybrid frameworks for dealing with uncertainties in knowledge-based systems. First, by combining the notions of picture fuzzy soft set, spherical fuzzy soft set, and T-spherical fuzzy soft set with a rough set, we introduce the novel models of Picture Fuzzy Soft Rough Sets (PcFSRSs), Spherical Fuzzy Soft Rough Sets (SpFSRSs), and T-Spherical Fuzzy Soft Rough Sets (TsFSRSs) for the parameterized fuzzy modelling of inconsistent data. Moreover, we explore some basic operational laws and fundamental properties of the developed models. We introduce a family of promising aggregation operators, namely, picture fuzzy soft rough ordered weighted averaging operator (PcFSROWAO), picture fuzzy soft rough ordered weighted geometric operator (PcFSROWGO), spherical fuzzy soft rough ordered weighted averaging operator (SpFSROWAO), spherical fuzzy soft rough ordered weighted geometric operator (SpFSROWGO), T-spherical fuzzy soft rough ordered weighted averaging operator (TsFSROWAO), and T-spherical fuzzy soft rough ordered weighted geometric operator (TsFSROWGO). We inspect some dominant peculiarities of these proposed operators inclusive of idempotence, boundedness and monotonicity. Further, we design a proficient approach using the proposed operators to untangle the complexity behind multi-attribute group decision making in real-world problems. We validate the effectiveness of the proposed technique by investigating its high potential in two real-world case studies. Finally, we demonstrate a comparative analysis of the proposed methodology with existing decision-making techniques to substantiate the accountability of the developed strategy.

Multi-attribute group decision making based on T-spherical fuzzy soft rough average aggregation operators / Akram, Muahmmad; Martino, Alessio. - In: GRANULAR COMPUTING. - ISSN 2364-4966. - 8:1(2023), pp. 171-207. [10.1007/s41066-022-00319-0]

Multi-attribute group decision making based on T-spherical fuzzy soft rough average aggregation operators

Alessio Martino
2023

Abstract

This research article aims at establishing the foundations of generalized hybrid frameworks for dealing with uncertainties in knowledge-based systems. First, by combining the notions of picture fuzzy soft set, spherical fuzzy soft set, and T-spherical fuzzy soft set with a rough set, we introduce the novel models of Picture Fuzzy Soft Rough Sets (PcFSRSs), Spherical Fuzzy Soft Rough Sets (SpFSRSs), and T-Spherical Fuzzy Soft Rough Sets (TsFSRSs) for the parameterized fuzzy modelling of inconsistent data. Moreover, we explore some basic operational laws and fundamental properties of the developed models. We introduce a family of promising aggregation operators, namely, picture fuzzy soft rough ordered weighted averaging operator (PcFSROWAO), picture fuzzy soft rough ordered weighted geometric operator (PcFSROWGO), spherical fuzzy soft rough ordered weighted averaging operator (SpFSROWAO), spherical fuzzy soft rough ordered weighted geometric operator (SpFSROWGO), T-spherical fuzzy soft rough ordered weighted averaging operator (TsFSROWAO), and T-spherical fuzzy soft rough ordered weighted geometric operator (TsFSROWGO). We inspect some dominant peculiarities of these proposed operators inclusive of idempotence, boundedness and monotonicity. Further, we design a proficient approach using the proposed operators to untangle the complexity behind multi-attribute group decision making in real-world problems. We validate the effectiveness of the proposed technique by investigating its high potential in two real-world case studies. Finally, we demonstrate a comparative analysis of the proposed methodology with existing decision-making techniques to substantiate the accountability of the developed strategy.
2023
Picture fuzzy soft rough sets, T-Spherical fuzzy soft rough sets, Ordered weighted geometric operators, Multi-attribute group decision making
Multi-attribute group decision making based on T-spherical fuzzy soft rough average aggregation operators / Akram, Muahmmad; Martino, Alessio. - In: GRANULAR COMPUTING. - ISSN 2364-4966. - 8:1(2023), pp. 171-207. [10.1007/s41066-022-00319-0]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/217057
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