Correlation coefficient measures for probabilistic single valued neutrosophic hesitant fuzzy sets and its application in supply chain management
نویسندگان
1 Department of Mathematics, Institute of Applied Sciences and Humanities, G.L.A. University, Mathura, U.P., India.
2 Department of Statistics, Maharshi Dayanand University, Rohtak, Haryana, India.
3 Department of Statistics, Maharshi Dayanand University, Rohtak, Haryana, India.
doi
10.22105/jfea.2025.475283.1602چکیده
Here, we introduce the Correlation Coefficient (CC) measures for Probabilistic Single-Valued Neutrosophic Hesitant Fuzzy Sets (PSVNHFSs), aiming to address the complexity of decision-making processes that involve uncertainty, hesitation, and probabilistic elements. The proposed measures offer a systematic approach to calculate the CC between PSVNHFSs by considering the Truth-Membership Hesitancy Degree (TMHD), Indeterminacy-Membership Hesitancy Degree (IMHD), and Falsity-Membership Hesitancy Degree (FMHD). Additionally, the paper introduces a Weighted Correlation Coefficient (WCC) method, allowing for differential weighting based on the risk preferences of Decision-Makers (DMs) and the relative importance of truth, indeterminacy, and falsity degrees. The proposed measure is applied to a Multi-Attribute Decision-Making (MADM) problem in Supply Chain Management (SCM), demonstrating its utility in selecting the best Supplier among multiple Suppliers. The application showcases the impact of attribute weights and risk preferences on supplier rankings, highlighting the measure's flexibility and robustness in real-world scenarios. The results indicate that the proposed CC and WCC measures can significantly enhance decision-making processes in environments characterized by uncertainty and hesitancy.
