Research Article GroupGeneralizedq-RungOrthopairFuzzySoftSets:New AggregationOperatorsandTheirApplications KhizarHayat , 1 RajaAqibShamim , 1 HussainAlSalman , 2 AbduGumaei , 3 Xiao-PengYang , 4 andMuhammadAzeemAkbar 5 1 Department of Mathematics, University of Kotli, Kotli, Azad Jammu and Kashmir, Pakistan 2 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia 3 Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz 6803, Yemen 4 School of Mathematics and Statistics, Hanshan Normal University, Chaozhou 521041, China 5 Lappeenranta-Lahti University of Technology (LUT), Department of Software Engineering, Lappeenranta 53851, Finland Correspondence should be addressed to Khizar Hayat; khizarhayat@uokajk.edu.pk and Abdu Gumaei; abdugumaei@gmail.com Received 10 July 2021; Revised 21 November 2021; Accepted 25 November 2021; Published 31 December 2021 Academic Editor: Giuseppe D'Aniello Copyright © 2021 Khizar Hayat et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 non- membership grades, which are not in the range of intuitionistic fuzzy sets. e 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. 1.Introduction 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. In some real-world difficulties where hu- manoid though attains reliable and unreliable information, the FS may not be sufficient to deal with underlying uncertainties. In 1986, another shape of the FS called intuitionistic fuzzy sets (IFSs) was authorized by Atanassov, which pro- vide a reliable grade μ(x) and unreliable grade ](x) for all x in the universe of discourse X. e IFSs are characterized by the sum μ(x)+ ](x) ≤ 1 and the degree of indeterminacy π(x)� 1 − μ(x)− ](x) [2]. Xu and Yager [3, 4] discussed the intuitionistic fuzzy value (IFV), which is an ordered pair of reliable and unreliable information for a component in the IFS on any x. Different rudiments of IFSs have been established such as aggregation operators [4], similarity and distance function [5, 6], and multicriteria decision-makings (MCDM) [7]. e aggregation operators are imperious in the MCDM process, which attains a shape of the measurable information by the accumulation of big data [8–10]. e IFSs enhance FSs in a meaningful approach, which is more capable of overcoming uncertainties, sharpless boundaries caused by the hesitation, and lack of assurance in human cognition. Xu and Zhao [11] extended a meaningful and insightful view on the information synthesis for MCDM Hindawi Mathematical Problems in Engineering Volume 2021, Article ID 5672097, 16 pages https://doi.org/10.1155/2021/5672097