Open Access

ARTICLE

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

by Khuram Ali Khan¹, Saba Mubeen Ishfaq¹, Atiqe Ur Rahman², Salwa El-Morsy^3,*

1 Department of Mathematics, University of Sargodha, Sargodha, 40100, Pakistan
2 Department of Mathematics, University of Management and Technology, Lahore, 54000, Pakistan
3 Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia

* Corresponding Author: Salwa El-Morsy. Email:

(This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)

Computer Modeling in Engineering & Sciences 2025, 142(1), 501-530. https://doi.org/10.32604/cmes.2024.057865

Received 29 August 2024; Accepted 29 October 2024; Issue published 17 December 2024

Abstract

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty, evaluating educational institutions can be difficult. The concept of a possibility Pythagorean fuzzy hypersoft set (pPyFHSS) is more flexible in this regard than other theoretical fuzzy set-like models, even though some attempts have been made in the literature to address such uncertainties. This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union, intersection, complement, OR- and AND-operations. Some results related to these operations are also modified for pPyFHSS. Additionally, the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results. Lastly, an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making (MADM) problems. A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system. The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS, as it generalizes the classical possibility fuzzy set-like theoretical models. Similarly, while considering significant evaluating factors, the flexibility of pPyFHSS is observed through structural comparison.

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Keywords

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