IEEE Access (Jan 2022)
Einstein Ordered Weighted Aggregation Operators for Pythagorean Fuzzy Hypersoft Set With Its Application to Solve MCDM Problem
Abstract
The Pythagorean fuzzy hypersoft set is the most generalized form of the Pythagorean fuzzy soft set used to resolve indeterminate and inexplicit information in the decision-making procedure, considering the parameters’ multi-sub-attributes. Aggregation operators execute a dynamic role in considering the two prospect sequences and eliminating anxieties from this perception. The hybrid form of Pythagorean fuzzy sets with hypersoft sets has appeared as a supportive structure in fuzzy mathematics and ensued as a convenient perspective in decision-making. This paper prolongs the Einstein-weighted ordered aggregation operators for the Pythagorean fuzzy hypersoft set, which proficiently contracts with tentative and confusing data. Experts are using the Pythagorean fuzzy hypersoft set in their quest to report indefinite and specific decision-making processes. It is an effective technique for enlarging unsure facts in decision-making. Some operational laws for the Pythagorean fuzzy hypersoft set have been projected in light of Einstein’s operations. Two innovative Einstein-ordered aggregation operators were established based on operational laws: Pythagorean fuzzy hypersoft Einstein-ordered weighted average and Pythagorean fuzzy hypersoft Einstein-ordered weighted geometric operators with their essential properties. Multi-criteria decision-making is imperative in overcoming barriers to real-world complications. However, conventional methods of multi-criteria decision-making regularly provide inconsistent results. The extended model appraisals recognized score values to regulate robotic agri-farming equated to prevalent methods, which is more useful for agribusiness. A numerical illustration of decision-making complications in real-life farming is deliberated to authenticate the established method’s supremacy and applicability. Based on the anticipated aggregation operators, a robust multi-criteria decision-making method has been presented, which delivers the most appropriate outcomes compared to existing multi-criteria decision-making techniques. The consequences show that the intended approach is more effective and stable in handling rough information based on the Pythagorean fuzzy hypersoft set.
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