Extended MABAC method based on divergence measures for multi-criteria assessment of programming language with interval-valued intuitionistic fuzzy sets

Programming languages (PLs) are symbolic instruction for writing programs, which have qualifications of evaluation of algorithm. The basic idea of programming languages (PLs) is to make it easier to write computer programs, now while that is true, what is often overlooked by language designers is that the other purpose, and maintaining its primary purpose, is to make it easier for people to read and understand programs. A programming language selection intrinsically is a multi-criteria decision-making (MCDM) problem. To handle the uncertainty of information in a MCDM problem, the theory of fuzzy sets is an effective tool. Interval-valued intuitionistic fuzzy sets (IVIFSs), which are characterized by the interval membership, non-membership and hesitancy functions have more flexibility to model the uncertainty of the MCDM problems than fuzzy sets (FSs). In this paper, a new integrated method based on multi-attributive border approximation area comparison (MABAC) method is proposed to handle MCDM problems with IVIFSs. This method is based on the IVIFS operators, some modifications in the classical MABAC method and a new procedure for calculation of the criteria weight. For calculation of criteria weight, we aggregate the subjective weights expressed by decision experts with the objective weights obtained from the proposed entropy and divergence measures method to obtain more realistic weights. Since the uncertainty is an inevitable characteristic of MCDM problems, the developed method can be a useful tool for decision making in an uncertain environment. To demonstrate the applicability of the developed method in the real-world MCDM problems, a programming language selection problem is taken. We perform a sensitivity analysis with different weights of criteria to show the stability of the proposed approach. This analysis shows that combining the subjective and objective weights can help to increase the stability of the proposed method with different weights of criteria. A comparison is also discussed between the results of the proposed and some existing methods for validating the proposed approach. This analysis shows that the proposed approach is efficient and well-consistent with the other methods.