Abstract
In recent years, q-rung orthopair fuzzy sets have been appeared to deal with an increase in the value of q > 1 , which allows obtaining membership and nonmembership grades from a larger area. Practically, it covers those membership and nonmembership grades, which are not in the range of intuitionistic fuzzy sets. The hybrid form of q-rung orthopair fuzzy sets with soft sets have emerged as a useful framework in fuzzy mathematics and decision-makings. In this paper, we presented group generalized q-rung orthopair fuzzy soft sets (GGq-ROFSSs) by using the combination of q-rung orthopair fuzzy soft sets and q-rung orthopair fuzzy sets. We investigated some basic operations on GGq-ROFSSs. Notably, we initiated new averaging and geometric aggregation operators on GGq-ROFSSs and investigated their underlying properties. A multicriteria decision-making (MCDM) framework is presented and validated through a numerical example. Finally, we showed the interconnection of our methodology with other existing methods.
Highlights
Zadeh originated the fuzzy set (FS) as an enlargement of the standard sets by the concept of inclusion of vague human judgements in computing situations [1]. e FS is indicated by the fuzzy information μ, which gives values from the unit close interval [0, 1] for each prospector x ∈ X. e idea of the FS plays an important role in the domain of soft computing, which manages vagueness, robustness, and partial truth
There is a huge capacity to exercise another view of group-based GIFSSs (GGIFSSs) aggregation operators because the q-rung orthopair fuzzy sets (q-ROFSs) relays the ambiguous information in higher productive ways than the GGIFSSs
Another important and fundamental point is to develop a different study to GGIFSSs that aggregate information concerning attributes until final ranking appears. us, we developed the group-based generalized q-ROFSSs (GGq-ROFSSs) and new aggregation operators through entire components in GGq-ROFSSs
Summary
Zadeh originated the fuzzy set (FS) as an enlargement of the standard sets by the concept of inclusion of vague human judgements in computing situations [1]. e FS is indicated by the fuzzy information μ, which gives values from the unit close interval [0, 1] for each prospector x ∈ X. e idea of the FS plays an important role in the domain of soft computing, which manages vagueness, robustness, and partial truth. GGIFSSs produce a deep and meaningful insight in the MCDM problem by merging aggregation operators [56] Another aspect of GGIFSS-based operators has been investigated by Hayat et al [59], which handle information in a collected form. The extended space of q-ROFSs is the general form to deal with any implicit information On this prospect, there is a huge capacity to exercise another view of GGIFSS aggregation operators because the q-ROFSs relays the ambiguous information in higher productive ways than the GGIFSSs. On this prospect, there is a huge capacity to exercise another view of GGIFSS aggregation operators because the q-ROFSs relays the ambiguous information in higher productive ways than the GGIFSSs Another important and fundamental point is to develop a different study to GGIFSSs that aggregate information concerning attributes until final ranking appears.
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