Identification of desalination and wind power plants sites using m-polar fuzzy Aczel–Alsina aggregation information

• Published in: Scientific reports Vol. 14; no. 1; pp. 409 - 22
• Main Authors: Rahman, Zia Ur, Ali, Ghous, Asif, Muhammad, Chen, Yufeng, Abidin, Muhammad Zain Ul
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1041; 639/705/1042; 704/242; 704/829; Datasets; Decision making; Desalination; Desalination plants; Fuzzy sets; Humanities and Social Sciences; Memberships; multidisciplinary; Neurosciences; Power plants; Science; Science (multidisciplinary); Set theory; Site selection; Wind power
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-50397-6

Real-world decision-making problems often include multi-polar uncertainties dependent on multi-dimensional attributes. The m -polar fuzzy ( m F) sets can efficiently handle such multi-faceted complications with T-norm based weighted aggregation techniques. The Aczel–Alsina T-norms offer comparatively flexible and accurate aggregation than the other well-known T-norm families. Consequently, this work introduced novel m F Aczel–Alsina aggregation operators (AOs), including weighted averaging ( m FAAWA, m FAAOWA, m FAAHWA) and weighted geometric ( m FAAWG, m FAAOWG, m FAAHWG) AOs. The fundamental properties, including boundedness, idempotency, monotonicity, and commutativity are investigated. Based on the proposed AOs, a decision-making algorithm is developed and implemented to solve two detailed multi-polar site selection problems (for desalination plant and for wind-power plant). Finally, a comparison with m F Dombi and m F Yager AOs reveals that different T-norm based AOs may yeild different solutions for the same problem.