CRITIC-MABAC approach with Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy Sets for Selecting the best Cloud Storage Service Alternative
Keywords:
Decision-making; CRITIC-MABAC; Probablistic linquistic fuzzy sets; $q$-rung orthopair fuzzy sets; Cloud storage serviceAbstract
Decision-making (DM) often experience challenges because of ambiguity and uncertainty in practical life. It is normally not easy to give clear values to data in such conditions. Probabilistic uncertain linguistic $q$-rung orthopair fuzzy sets (PUL$q$-ROFSs) constitute a flexible and applicable instrument that allows coping with the inherent ambiguity and fuzziness. These sets provide a wider methodology for addressing complex DM issues. A powerful solution for addressing such issues is the decomposition of the advantages of the multi-attributive border approximation area comparison (MABAC) method and the criteria importance through inter-criteria correlation (CRITIC) method. CRITIC is efficient when distributing weights of criteria based on interrelations, meanwhile MABAC is famous for its high rank towards options for their distance from the approximation area. A combination of these techniques leads to a thorough and systematic framework for the solution of uncertain DM problems. Based on this background, we extend the CRITIC-MABAC methodology to PUL$q$-ROFSs. This methodology can tackle a real-world use case: choosing the best cloud storage service alternative. We use the MABAC technique to rank alternatives and the CRITIC method to weight criteria with the aim of making our results valid and meaningful. We confirm the practical aspect of the methodology and evaluate its performance on a real-world DM situation, comparing the results with those yielded by previous techniques. Our findings highlight the impressive performance and the applicability of the presented CRITIC-MABAC approach.
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Copyright (c) 2025 Uzma Ahmad, Saira Hameed, Muhammad Faisal Shabir, Ayesha Khan
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