Ambiguous Fuzzy Einstein Ordered Averaging Operator: Application to the classification of Power Generation methods

The classification of power generation methods (PGMs) can be treated as multi-criteria-decision-making-procedure (MCDMP) as it is affected by many. The information generations are always big task to stockholders due to the incomplete information as it is very hard to extract the exact information. Most of the things are qualitative and quantitative. Therefore, to study systems under uncertainty fuzzy set (FS) and intuitionistic FS (IFS) are presented as a breakthrough. The Ambiguous Fuzzy set theory (AFST) is presented as the generalization of FS consisting of four types of membership degrees. The AFST is powerful than the FS and IFS due to four different degrees of memberships. This study presents the Einstein Ordered Weighted Averaging (OWA) Operator (EOWAO) based on the Einstein Sum (ES) and OWA under the domain of AFST, which is termed as “AFSeow”. Then, to apply the AFS eow operator to real life problems, a framework is presented to include it in the information fusion of decision making (DM) process. Moreover, the classification of Power Generation methods (PGMs) is carried out using the proposed operator and proposed framework to show its applicability. The obtained results and comparative analysis show the feasibility and validity of the AFSEow operator.

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