Model for multiple attribute decision making based on the Einstein correlated information fusion with hesitant fuzzy information

Abstract

We investigate the multiple attribute decision making (MADM) problems in which attribute values take the form of hesitant fuzzy information. Firstly, some operational laws of hesitant fuzzy elements and score function of hesitant fuzzy elements are introduced. In this paper, we utilize Einstein operations to develop some hesitant fuzzy correlated aggregation operators: hesitant fuzzy Einstein correlated averaging (HFECA) operator and hesitant fuzzy Einstein correlated geometric (HFECG) operator. The prominent characteristic of the operators is that they can not only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have applied the HFECA and HFECG operators to multiple attribute decision making with hesitant fuzzy information. Finally an illustrative example has been given to show the developed method.