Roughness Based Aczel Alsina Aggregation Operators for Multi Attribute Group Decision Making Using Pythagorean Fuzzy Information

Roughness Based Aczel Alsina Aggregation Operators for Multi Attribute Group Decision Making Using Pythagorean Fuzzy Information | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Roughness Based Aczel Alsina Aggregation Operators for Multi Attribute Group Decision Making Using Pythagorean Fuzzy Information Nisar Ali, Muhammad Rizwan Khan, Kifayat Ullah, Zeeshan Ali, Dragan Pamucar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7225392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Considering classical set theory, asymmetric and ambiguous information management is challenging. In fuzzy set (FS) theory, Aczel-Alsina aggregation operators (AOs) are new developments. However, when experts try to use classical set theory for rough fuzzy structures, these concepts fail to handle such values, as fuzzy irregular frameworks use upper and lower approximation spaces. However, data loss is possible when a Pythagorean FS (PyFS) is enclosed, but the issue can be solved by a Pythagorean fuzzy (PyF) rough (PyFR) set. By taking motivation from these newly introduced operational laws, PyFR Aczel-Alsina (PyFRAA), T-conorm (TCNM), and T-norm (TNM), this article firstly introduces the PyFRAA operations for PyF rough values. Secondly, based on newly developed Aczel-Alsina (AA) operations, we have proposed PyFRAA power-weighted averaging (PyFRAAPWA) and PyFRAA power-weighted geometric (PyFRAAPWG) AOs. These AOs help aggregate asymmetric and awkward data in real-life issues. The suggested AOs in medical diagnosis and multi-attribute group decision-making (MAGDM) are suitable techniques that can help in medical diagnosis and decision-making theory. We established a real-life numerical example with a detailed algorithm to highlight the effectiveness and universality of the presented AOs in the medical sciences and the selection of the finest treatment method. To deliberate the diversity and significance of the developed AOs, we offer a comparative investigation with the present AOs. Physical sciences/Engineering Physical sciences/Mathematics and computing Pythagorean fuzzy rough set Aczel-Alsina T-norm and T-conorm aggregation operators decision-making multi-attribute group decision-making Full Text Additional Declarations No competing interests reported. Supplementary Files SupplimentrtyFileforSR.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 24 Dec, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 11 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 11 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7225392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501149711,"identity":"ac08cb8e-ffcf-421e-a378-c6430d562262","order_by":0,"name":"Nisar Ali","email":"","orcid":"","institution":"Riphah International University Lahore","correspondingAuthor":false,"prefix":"","firstName":"Nisar","middleName":"","lastName":"Ali","suffix":""},{"id":501149712,"identity":"6d377c40-1d45-4baa-8f06-215f26f219c5","order_by":1,"name":"Muhammad Rizwan Khan","email":"","orcid":"","institution":"Riphah International University 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