Intuitionistic fuzzy divergence measure-based multi-criteria decision-making method

Divergence measure is a significant tool for evaluating the amount of discrimination for IFSs. Since then it has acquired concentration for their applications in different areas. In this paper, we utilize the conception of Jensen–Shannon divergence to propose new measures called Jensen-exponential divergence for measuring the discrimination between intuitionistic fuzzy sets (IFSs) and demonstrate some very elegant properties, which show its strength for applications point of view. Next, a multi-criteria decision-making (MCDM) problem for IFSs that describes information about options with respect to criteria is studied. A technique that employs the relative comparisons for IFSs (that uses all the constraints, viz. membership, non-membership and hesitancy degrees) based on the advantage and disadvantage scores of the options with respect to criterion, where criterion weights are completely unknown, is used. In addition, the score functions are applied to evaluate the strength and worst scores leading to the satisfaction degree of the options. A multi-objective optimization model for optimal weights of the criterion that maximizes the satisfaction degree of each option is constructed. Energy resources play an important role in the social and economic development of the countries. Due to the industrialization, population growth and urbanization, the demand of energy is increasing gradually and this requires the selection of most suitable energy resource for economic development of the countries. The proposed MCDM method is presented to choose the most appropriate energy alternative among set of renewable energy alternatives. In this real case study, the decision makers provide their opinions in terms of linguistic variables because it is tricky to portray exact numerical values during the evaluation of energy alternatives. Finally, a comprehensive comparison is prepared to express the effectiveness of the technique over the existing techniques for the IF MCDM problems.