For the first time, Atanassov developed Intuitionistic fuzzy sets (IFSs) as an extended form of fuzzy sets (FSs), which has degree of membership, non-membership and hesitancy. Divergence is the difference between two IFSs and it is a particular case of dissimilarity and Intuitionistic Fuzzy-distance (IF-distance). It is a modern way of decision making and can differentiate qualities of two sets. The importance of divergence measure is the most concern in different areas including segmentation of images and decision making. We suggested a novel and simple divergence between two IFSs which rank the MCDM (Multicriteria Decision Making) systems easily. Particular features of our proposed divergence are monotonicity (it fulfills monotonic properties) and involve simple calculations. So far best divergence has been calculated by Jensen-Renyi Divergence but it failed to some pattern sets. Our proposed divergence holds on those sets, this leads toward better decision making.

Finally, we utilize our proposed divergence in practical problems related to hotel management involving complex MCDM. Numerical simulations reveal that our proposed method is effective, applicable and well suited in managing different real life issues.

Keywords:  Intuitionistic Fuzzy Sets, Multicriteria Decision Making, Linguistic Variables,                              Likert Scale.