The main purpose of this study is to develop a way of solving multiattribute decision‐making problems (MADM), where the value of an attribute appears as picture hesitant fuzzy number and the attribute weight information is not entirely known. So as to acquire attribute weight vector, we build up a model of optimization that relies on the basic ideal of the conventional grey relational analysis method, by which attribute weight can be resolved. We build up another model of optimization for the uncommon circumstance where the attribute weight information is totally unknown. By solving this model, we get a basic and definite equation which can be utilized to be determined attribute weight. The grey relation degree between each alternative and positive ideal solution (PIS) and negative ideal solution (NIS) are determined. At the same time, a degree of relative relational is defined to decide all alternative order of ranking by ascertaining the grey relation degree to both the PIS and NIS. To examine the produced approach and to show its effectiveness and applicability an illustrative examples are given. A comparative analysis with pre‐existing MADM methods are considered to show the predominance of the proposed strategy.

The fuzzy set has its own limitations due to the membership function only. The fuzzy set does not describe the negative aspects of an object. The Fermatean fuzzy set covers the negative aspects of an object. The complex Fermatean fuzzy set is the most effective tool for handling ambiguous and uncertain information. The aim of this research work is to develop new techniques for complex decision-making based on complex Fermatean fuzzy numbers. First, we construct different aggregation operators for complex Fermatean fuzzy numbers, using Einstein t-norms. We define a series of aggregation operators named complex Fermatean fuzzy Einstein weighted average aggregation (CFFEWAA), complex Fermatean fuzzy Einstein ordered weighted average aggregation (CFFEOWAA), and complex Fermatean fuzzy Einstein hybrid average aggregation (CFFEHAA). The fundamental properties of the proposed aggregation operators are discussed here. The proposed aggregation operators are applied to the decision-making technique with the help of the score functions. We also construct different algorithms based on different aggregation operators. The extended TOPSIS method is described for the decision-making problem. We apply the proposed extended TOPSIS method to MAGDM problem “selection of an English language instructor”. We also compare the proposed models with the existing models.

Fermatean fuzzy sets (FFSs) have piqued the interest of researchers in a wide range of domains. The striking framework of the FFS is keen to provide the larger preference domain for the modeling of ambiguous information deploying the degrees of membership and nonmembership. Furthermore, FFSs prevail over the theories of intuitionistic fuzzy sets and Pythagorean fuzzy sets owing to their broader space, adjustable parameter, flexible structure, and influential design. The information measures, being a significant part of the literature, are crucial and beneficial tools that are widely applied in decision‐making, data mining, medical diagnosis, and pattern recognition. This paper aims to expand the literature on FFSs by proposing many innovative Fermatean fuzzy sets‐based information measures, namely, distance measure, similarity measure, entropy measure, and inclusion measure. We investigate the relationship between distance, similarity, entropy, and inclusion measures for FFSs. Another achievement of this research is to establish a systematic transformation of information measures (distance measure, similarity measure, entropy measure, and inclusion measure) for the FFSs. To accomplish this aim, new formulae for information measures of FFSs have been presented. To demonstrate the validity of the measures, we employ them in pattern recognition, building materials, and medical diagnosis. Additionally, a comparison between traditional and novel similarity measures is described in terms of counter‐intuitive cases. The findings demonstrate that the innovative information measures do not include any absurd cases.