In this paper, we define some Einstein operations on cubic fuzzy set (CFS) and develop three arithmetic averaging operators, which are cubic fuzzy Einstein weighted averaging (CFEWA) operator, cubic fuzzy Einstein ordered weighted averaging (CFEOWA) operator and cubic fuzzy Einstein hybrid weighted averaging (CFEHWA) operator, for aggregating cubic fuzzy data. The CFEHWA operator generalises both the CFEWA and CFEOWA operators. Furthermore, we develop various properties of these operators and derive the relationship between the proposed operators and the exiting aggregation operators. We apply CFEHWA operator to multiple attribute decision-making with cubic fuzzy data. Finally, a numerical example is constructed to demonstrate the established approach.

Pythagorean fuzzy sets (PFSs), hesitant fuzzy sets (HFSs) and intuitionistic hesitant fuzzy sets (IHFSs) have attracted more and more scholars’ attention due to their powerfulness in expressing vagueness and uncertainty. Intuitionistic hesitant fuzzy set satisfies the condition that the sum of its membership’s degrees is less than or equal to one. However, there may be a situation where the decision maker may provide the degree of membership and nonmembership of a particular attribute in such a way that their sum is greater than 1. To overcome this shortcoming, in this paper we introduce the concept of Pythagorean hesitant fuzzy set (PHFS) which is the generalization of intuitionistic hesitant fuzzy set under the restriction that the square sum of its membership degrees is less than or equal to 1. We discuss some properties of PHFS. We define score and accuracy degree of the Pythagorean hesitant fuzzy numbers (PHFNs) for comparison in Pythagorean hesitant fuzzy numbers. Also in decision making with PHFSs, aggregation operators play a very important role since they can be used to synthesize multidimensional evaluation values represented as Pythagorean hesitant fuzzy valued into collective values. We develop distance measure between PHFNs. Under PHFS environments, we develop aggregation operators namely, Pythagorean hesitant fuzzy weighted averaging (PHFWA), Pythagorean hesitant fuzzy weighted geometric (PHFWG). We develop the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in Pythagorean hesitant fuzzy numbers and the information about attribute weights is incomplete. The main advantage of these operators is that it is to provide more accurate and precious results. Furthermore, we developed these operators are applied to decision-making problems in which experts provide their preferences in the Pythagorean hesitant fuzzy environment to show the validity, practicality and effectiveness of the new approach.

Q-Rung orthopair fuzzy soft set handles the uncertainties and vagueness by membership and non-membership degree with attributes, here is no information about the neutral degree so to cover this gap and get a generalized structure, we present hybrid of picture fuzzy set and q-rung orthopair fuzzy soft set and initiate the notion of q-rung orthopair picture fuzzy soft set, which is characterized by positive, neutral and negative membership degree with attributes. The main contribution of this article is to investigate the basic operations and some averaging aggregation operators like q-rung orthopair picture fuzzy soft weighted averaging operator and q-rung orthopair picture fuzzy soft order weighted averaging operator under the environment of q-rung orthopair picture fuzzy soft set. Moreover, some fundamental properties and results of these aggregation operators are studied, and based on these proposed operators we presented a stepwise algorithm for MADM by taking the problem related to medical diagnosis under the environment of q-rung orthopair picture fuzzy soft set and finally, for the superiority we presented comparison analysis of proposed operators with existing operators.