An innovative approach to hesitant bipolar fuzzy soft sets in multi-criteria group decision-making

• Published in: Advances in computational intelligence Vol. 5; no. 3; p. 4
• Main Authors: Das, Ajoy Kanti, Patra, Suman, Granados, Carlos
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2025; Springer Nature B.V
• Subjects: Alternative energy sources; Alternatives; Artificial Intelligence; Computational Intelligence; Credibility; Decision making; Engineering; Fuzzy sets; Hydroelectric plants; Linguistics; Machine Learning; Methods; Multiple criterion; Original Article; Ranking; Set theory; Solar power generation; Wind power; Decision-making; Bipolar fuzzy set; Hesitant bipolar fuzzy soft sets; Multi-criteria decision-making; Credibility score; Soft set; Hesitant fuzzy set; Fuzzy set; Sustainable energy project
• ISSN: 2730-7794; 2730-7808
• DOI: 10.1007/s43674-025-00082-0

This paper explores the integration of hesitant bipolar fuzzy soft sets (HBFSS) into multi-criteria group decision-making (MCGDM), aiming to enhance decision precision and resolve uncertainties in expert evaluations. We introduce a novel decision-making framework that combines the root mean square deviation (RMSD) method with a credibility score, capturing both the proximity to ideal solutions and the consistency of expert opinions. The process is applied to a sustainable energy project selection problem, showcasing its efficacy in ranking alternatives such as solar farm, wind park, and hydroelectric plant. A comparative analysis with the existing model highlights the limitations of traditional approaches, including the failure to differentiate alternatives with similar scores and neglecting expert consistency. Our results demonstrate that the proposed RMSD-Credibility approach offers a more nuanced, consistent, and precise ranking, improving decision quality in complex, uncertain environments. This paper contributes to advancing decision-making under fuzzy and uncertain conditions by providing an innovative aggregation mechanism tailored to the challenges of real-world multi-criteria problems.