Ranking Higher Education Institutions Using Entropy–VIKOR with Generalized Pentagonal Intuitionistic Fuzzy Numbers

Ranking the higher educational institutes is a very challenging task. In ranking higher educational institutions, many criteria and sub-criteria must be considered. These criteria may be beneficial or non-beneficial, which also increases the problem's complexity. Here, our aim is to develop an advanced framework to determine the rankings of higher educational institutions, incorporating uncertainty in the environment and the dataset. Here, we will take the help of Generalized Pentagonal Intuitionistic Fuzzy Numbers (GPIFNs), which are an extension of Pentagonal Fuzzy Numbers (PFNs), to capture the uncertainty of the model. For this purpose, we will include two popular MCDM methodologies: the Entropy weighted method for evaluating the criteria weights and the VIKOR method for ranking alternatives across different higher educational institutions. In this model, the opinions of different decision experts will be taken in linguistic terms as a dataset and will further be converted to GPIFNs. Lastly, sensitivity analysis and comparative analysis will be performed to assess the stability and robustness of the results obtained with this model. This model will be very effective for making policies for the advancement of higher educational institutions, and it will provide a clear view of the strengths and weaknesses of an institution to the administrator so that they can take necessary strategies to improve the quality of education.