Multicriteria group decision making for COVID-19 testing facility based on picture cubic fuzzy aggregation information

Abstract

The information aggregation of cubic fuzzy numbers and picture fuzzy numbers have played an important role in decision making. This paper introduces a novel approach to address the problem of testing facility of COVID-19 under picture cubic fuzzy environment. As the picture cubic fuzzy set is a generalized fuzzy structure to handle more uncertainty and ambiguity in decision making problems We discuss its various properties. Based on geometric aggregation operators and Hamacher operations, we introduce some Hamacher geometric aggregation operators under picture cubic fuzzy information. Namely, picture cubic fuzzy Hamacher weighted geometric aggregation operator, picture cubic fuzzy Hamacher hybrid geometric operator, picture cubic fuzzy Hamacher order weighted geometric aggregation operator. Discuss some properties of the defined operators. To verify the importance of the proposed operators, develop multicriteria group decision making (MCGDM) algorithm under picture cubic fuzzy environment and apply this strategy for the selection of an authentic laboratory for COVID-19 test. Further to validate the supremacy of our proposed operators, we present a comparative analysis with pre-existing aggregation operators. Results show that the proposed technique is more effective and suitable for MCGDM problems.

An efficient spherical fuzzy MEREC–CoCoSo approach based on novel score function and aggregation operators for group decision making

“…In this regard, a novel fuzzy extension of FS called picture fuzzy sets (PFS) (Cuong 2014 ) is originated with the help of considering four characteristic functions involving membership, non-membership, neutrality and abandonment. Since then, research on PFS has been widely used in decision analysis, pattern recognition and other uncertainty analysis due to its significant advantage of being able to model uncertainty more comprehensively (Almulhim and Barahona 2023 ; Haktanir and Kahraman 2022 ; Joshi and Kumar 2022 ; Kadian and Kumar 2022 ; Lin et al 2020 ; Muneeza et al 2022 ; Qin et al 2020 , 2021 ). Many achievements on PFS have proved that PFS is suitable for capturing uncertain, inaccurate and inconsistent information in decision analysis.…”