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

Tripti Basuri; Kamal Hossain Gazi; Srabani Guria Das; Sankar Prasad Mondal

Authors

Tripti Basuri Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, West Bengal, India Author https://orcid.org/0000-0003-4690-2598
Kamal Hossain Gazi Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, West Bengal, India Author https://orcid.org/0000-0003-4690-2598
Srabani Guria Das Department of Basic Science, NSHM Knowledge Campus, Durgapur, West Bengal, India Author https://orcid.org/0000-0003-4690-2598
Sankar Prasad Mondal Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, West Bengal, India Author https://orcid.org/0000-0003-4690-2598

Keywords:

Higher educational institution ranking, Generalized pentagonal intuitionistic fuzzy numbers, Fuzzy sets, MCDM, Decision making, Entropy, VIKOR

Abstract

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.

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