Abstract: A novel MAGDM method based on the correlation coefficient and hesitancy degrees under interval-valued intuitionistic fuzzy environmentis proposed. Firstly, the conceptions of individual hesitancy degree and group hesitancy degree are put forward to overcome the restrictions of the methodology of "majority rule". Secondly, the correlation coefficients of IVIFN are utilized to measure the similarity instead of distance functions in order to alleviate the drawbacks that donot consider the parameters of hesitancy. Thirdly, the subjective assignment methods are extended on utilizing the IVIFN values in order to better address the uncertainties in the MAGDM problem. Finally, TOPSIS and LP optimization method are used to calculate the optimal attribute weight, which ensures more optimal and accurate weights. A real-word application example has been presented to demonstrate the working of the proposed methodology. Moreover, a thorough comparison has been done with related existing works in order to show the validity of this methodology.

Key words: hesitancy degrees, correlation coefficients, similarity measurement, interval-value intuitionistic fuzzy Numbers, multi-attribute group decision making