Confidence Level-Based Hamacher Aggregation Operators for Sustainable Furniture Supplier Selection using p,q-Quasirung Orthopair Fuzzy Sets
Utpal Mandal
1
(
Department of Mathematics and Statistics, Banasthali Vidyapith, Rajasthan, India.
)
Arnab Kumar De
2
(
Department of Mathematics, Government College of Engineering and Textile, Serampore, India.
)
Mijanur Rahaman Seikh
3
(
Department of Mathematics, Kazi Nazrul University, Asansol, India.
)
الکلمات المفتاحية: Multi-criteria group decision-making, p, q-QOFSs, Hamacher aggregation operators, Confidence levels, Supplier selection.,
ملخص المقالة :
Selecting sustainable furniture suppliers for universities is a complex decision-making problem that must balance environmental, economic, and social factors under conditions of uncertainty. As higher education institutions increasingly adopt green procurement policies, the need for structured, data-driven evaluation methods becomes more pressing. This necessitates robust models capable of handling imprecision and reflecting the trustworthiness of expert opinions. Traditional methods often fall short in handling the vagueness inherent in expert evaluations. To address this, we adopt the recently introduced p,q-quasirung orthopair fuzzy sets (p,q-QOFSs), which provide a more flexible framework for modeling imprecise information. This study proposes novel confidence level-based Hamacher weighted averaging and geometric aggregation operators for p,q-QOFSs to incorporate the reliability of expert judgments into the decision-making process. Using these operators, we develop a robust multi-criteria group decision-making (MCGDM) model for sustainable supplier selection. The model is validated through a real-world case study involving three experts assessing four suppliers against eight sustainability criteria. Comparative analysis with existing methods highlights the superior performance of the proposed approach, while sensitivity analysis confirms its stability and robustness across varying parameter settings. The incorporation of confidence levels not only enhances the credibility of the aggregated evaluations but also allows for more informed and nuanced decision outcomes.
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