Some Einstein Geometric Aggregation Operators for q-Rung Orthopair Fuzzy Soft Set With Their Application in MCDM

q-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.