M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/52
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Yager’s Prioritized Model for Multi-Attribute Group Decision-Making Using p,q-Quasirung Orthopair Fuzzy Information(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Nisha Ashfaq; CIIT/SP24-RMT-014/LHR; Dr. Atiq-ur-Rehman; LHR TP 10075This study presents a multi-attribute group decision-making (MAGDM) approach based on Yager’s prioritized model under the framework of p,q-quasirung orthopair fuzzy sets (p,q-QROFSs). The proposed method effectively handles uncertainty and vagueness in decision-making environments while considering the priority relationships among decision attributes. Aggregation operators are developed to combine experts’ evaluations expressed in p,q-quasirung orthopair fuzzy information. The Yager prioritized mechanism is incorporated to reflect the relative importance of attributes and decision-makers. The effectiveness and practicality of the proposed approach are demonstrated through a numerical example and comparative analysis. Results indicate that the method provides reliable and flexible decision support for complex group decision-making problems involving uncertain information.Item Supplier Selection Using Multi-Attributive Border Approximation Area Comparison (MABAC) Model in Different Fuzzy Environments(Library Information Services COMSATS University Lahore Campus, 2024-03-17) Saleha Javed; SP23-RMT-029; Dr. Atiq-ur-Rehman; LHR TP 9565The main objective of this study is to develop a supplier selection approach that incorpo rates Multi-Attribute Border Approximation Area Comparison (MABAC) in three different fuzzy environments: Intuitionistic fuzzy MABAC, fuzzy MABAC, and Pythagorean fuzzy MABAC. By addressing uncertainties and assessments related to qualitative aspects such as quality, responsiveness, and reliability, the technique aims to optimize competing crite ria. Initially, the fuzzy MABAC methods utilize fuzzy evaluations to determine supplier ranks. Subsequently,fuzzy technologies that address imprecision in decision-making are integrated to assess the consistency of supplier rankings. The robustness of the approach is demonstrated by the consistent rankings of the best and worst suppliers across all three fuzzy environments. This innovative approach provides a reliable method for selecting sup pliers, ensuring that businesses can make informed decisions that consider both qualitative and quantitative factors even in uncertain circumstances.