Optimizing Supply Chain Efficiency Through Multi-Head Swift Recursive Neural Network and Levy flight Boosted Dragonfly Model

Optimizing Supply Chain Efficiency Through Multi-Head Swift Recursive Neural Network and Levy flight Boosted Dragonfly Model | 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 Research Article Optimizing Supply Chain Efficiency Through Multi-Head Swift Recursive Neural Network and Levy flight Boosted Dragonfly Model Norah Nasser M ALQutaim, Abdulrhman ALShareef, Syed Hamid Hassan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5671581/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Supply chain optimization is a significant challenge faced by modern industries. This necessitates the use of advanced data analytics and machine learning techniques, which can enhance forecasting accuracy, minimize inventory usage, and improve logistical efficiency. Therefore, this study introduces a novel framework for improving supply chain efficiency using data-driven analysis and advanced modeling techniques. The process begins with the collection of relevant supply chain data, which includes historical data on analysis, forecasting, and time series data. The core of the methodology is the integration of a novel Levy Flight Binary Dragonfly Optimization strategy with a Multi-Head SwifT recursive neural network model. One of the most popular architectures combines recurrent networks, such as SwiftRNN, with Multi-Head Attention which aims to capture time dependencies and emphasize essential patterns in data. Enhancing model performance involves utilizing optimization techniques like Binary Dragonfly Optimization with Levy Flight. This method optimally tunes hyperparameters by striking a balance between exploration and exploitation, mitigating the risk of local minima, and yielding more robust outcomes. Empirical results demonstrate significant improvements in forecasting accuracy, better inventory management, and enhanced logistics optimization, with a 0.134 reduction in prediction error and improved operational metrics outperformed the existing models, with an impressive 95% on-time delivery rate, 2% defect rate, and 95% logistic cost efficiency rate. Overall, the proposed framework exhibits significant potential for optimizing supply chain efficiency. Supply chain Multi-Head Attention Binary Dragonfly Optimization with Levy Flight Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5671581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392890449,"identity":"5561e328-bb19-4813-b208-5d38ff5a9765","order_by":0,"name":"Norah Nasser M ALQutaim","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Norah","middleName":"Nasser M","lastName":"ALQutaim","suffix":""},{"id":392890450,"identity":"2006227c-5262-4480-a2dd-bee3b8945117","order_by":1,"name":"Abdulrhman ALShareef","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrhman","middleName":"","lastName":"ALShareef","suffix":""},{"id":392890453,"identity":"0429a937-4358-4ac2-bf19-1ac79e42079a","order_by":2,"name":"Syed Hamid Hassan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDACCR4wJScBpthI0GJMupbEGURrkZ/de/Djj5o76TPbzxgwfCg7zCDffgC/FoM755KleY49y53Nk2PAOOPcYQaDMwkEtEjkGEgzsB3OnceQY8DM2wbUwkBAi/yMHOOfP/4dTpfjf2PA/BeoRb7/AQHP3MgxkwAaniANtI6ZEaiF4QYhhwG1WPP2HTacOeNZwcGec+k8BjcI2AJy2M0f3w7LS5xP3vjgR5m1nHw/AVtQwAEg5iFB/SgYBaNgFIwCXAAA2mtDCWK72jUAAAAASUVORK5CYII=","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":true,"prefix":"","firstName":"Syed","middleName":"Hamid","lastName":"Hassan","suffix":""}],"badges":[],"createdAt":"2024-12-18 18:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5671581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5671581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93085341,"identity":"2a672585-6f77-4b2d-a720-5d9fcff68817","added_by":"auto","created_at":"2025-10-09 02:38:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1016977,"visible":true,"origin":"","legend":"","description":"","filename":"NorahPaper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5671581/v1_covered_3c3b1d13-88e6-45fe-82cc-f921fecfc411.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Supply Chain Efficiency Through Multi-Head Swift Recursive Neural Network and Levy flight Boosted Dragonfly Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Supply chain, Multi-Head Attention, Binary Dragonfly Optimization with Levy Flight","lastPublishedDoi":"10.21203/rs.3.rs-5671581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5671581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSupply chain optimization is a significant challenge faced by modern industries. 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