WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Citation - WoS: 31Citation - Scopus: 35Some Einstein Geometric Aggregation Operators for Q-Rung Orthopair Fuzzy Soft Set With Their Application in Mcdm(Ieee-inst Electrical Electronics Engineers inc, 2022) Ali, Rifaqat; Awrejcewicz, Jan; Siddique, Imran; Jarad, Fahd; Iampan, Aiyared; Zulqarnain, Rana Muhammadq-rung orthopair fuzzy soft sets (q-ROFSS) is a progressive form for orthopair fuzzy sets. It is also an appropriate extension of intuitionistic fuzzy soft sets (IFSS) and Pythagorean fuzzy soft sets (PFSS). The strict prerequisite gives assessors too much autonomy to precise their opinions about membership and non-membership values. The q-ROFSS has a wide range of real-life presentations. The q-ROFSS capably contracts with unreliable and ambiguous data equated to the prevailing IFSS and PFSS. It is the most powerful method for amplifying fuzzy data in decision-making. The hybrid form of orthopair q-rung fuzzy sets with soft sets has emerged as a helpful framework in fuzzy mathematics and decision-making. The hybrid structure of q-rung orthopair fuzzy sets with soft sets has occurred as an expedient context in fuzzy mathematics and decision-making. The fundamental impartial of this research is to propose Einstein's operational laws for q-rung orthopair fuzzy soft numbers (q-ROFSNs). The core objective of this research is to develop some geometric aggregation operators (AOs), such as q-rung orthopair fuzzy soft Einstein weighted geometric (q-ROFSEWG), and q-rung orthopair fuzzy soft Einstein ordered weighted geometric (q-ROFSEOWG) operators. We will discuss the idempotency, boundedness, and homogeneity of the proposed AOs. Multi-criteria decision-making (MCDM) is dynamic in dealing with the density of real-world complications. Still, the prevalent MCDM techniques consistently deliver irreconcilable outcomes. Based on the presented AOs, a strong MCDM technique is deliberate to accommodate the flaws of the prevailing MCDM approaches under the q-ROFSS setting. Moreover, an inclusive comparative analysis is executed to endorse the expediency and usefulness of the suggested method with some previously existing techniques. The outcomes gained through comparative studies spectacle that our established approach is more capable than prevailing methodologies.Article Citation - WoS: 62Citation - Scopus: 70The Novel Augmented Fermatean Mcdm Perspectives for Identifying the Optimal Renewable Energy Power Plant Location(Elsevier, 2022) Parthasarathy, Thirumalai Nallasivan; Pragathi, Subramaniam; Shanmugam, Ponnan; Baleanu, Dumitru; Ahmadian, Ali; Kang, Daekook; Narayanamoorthy, SamayanThe Fermatean fuzzy set has been authorized as a suitable tool for the uncertainty and vagueness of information by augmenting the spatial space of acceptance membership and non-acceptance membership degrees of both intuitionistic and Pythagorean fuzzy sets. Solar energy does not emit any hazardous gases into the atmosphere, making it one of the most effective strategies to reduce global warming in the environment. Under a variety of circumstances, finding a spot for a photovoltaic solar power plant might be difficult. As a result, we experiment with multi-criteria decision-making (MCDM) techniques. We presented a hybrid technique based on the PV-SPSS method based on the Removal Effects of Criteria (MEREC) and Multiple Objective Optimization on the Basis of Ratio Analysis with Full Multiplicative Form (MULTIMOORA) analysis. The MEREC approach is used to calculate the weightage of each attribute, and MULTIMOORA is used to find the ranking of the alternatives. Also, a new rectified generalized score function determines the score value of FFSs. Culmination: the validity of the result is assessed by implementing the existing MCDM approaches and by changing the criterion weight.