Maestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines

Maestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines | 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 Maestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines Minhui Xie, Shujian Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6675902/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 Manufacturing lines face numerous challenges in task recognition and optimization, particularly due to their dynamic nature. To tackle these issues, we introduce Maestro, a multi-agent enhanced system that utilizes a decentralized agent architecture. Each agent within Maestro specializes in specific facets of the manufacturing process, which fosters efficient collaboration and data sharing. By employing machine learning algorithms, Maestro dynamically recognizes tasks, allowing it to adapt to real-time fluctuations in manufacturing conditions. Furthermore, this system merges task recognition with advanced optimization algorithms, significantly enhancing production efficiency and minimizing downtime. Comprehensive simulations and experiments conducted across various manufacturing environments validate the framework, revealing marked improvements in task completion rates and resource utilization. Maestro stands as a pivotal advancement in creating a more agile and intelligent manufacturing ecosystem. Computer Architecture and Engineering Neural Visualization Human Center Interaction User Feedback Full Text Additional Declarations The authors declare no competing interests. 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-6675902","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457314041,"identity":"8a5d2b1c-55dc-4fac-8d9f-87a4c4c13654","order_by":0,"name":"Minhui Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACZgY2BgYDBjkGdhDPACrKQ4QWYyCDWC0MIC0MDIkNzMhi+LTotvMee8xTcDi9v5nHdHNBAYO87owExgdv23BrMTvMl27MY5CWO+Mwj9ntGQYMhttuJDAbzsWrhcdMmsfAJrcBpIXHgCHB7EYCmzQvYS0S6fJIWth/E6HFJsEA2RZmQlok5xikGW48zFYG1CJhuO3Mw2bJOefwaDl/xkzizZ/D8nLHm7fd5vljI292PPnghzdluLWgAwkgZmwgXv0oGAWjYBSMAqwAAOvGRN0OgpCnAAAAAElFTkSuQmCC","orcid":"","institution":"Carnegie Mellon University","correspondingAuthor":true,"prefix":"","firstName":"Minhui","middleName":"","lastName":"Xie","suffix":""},{"id":457314042,"identity":"35ff581a-1874-42a5-a9d5-53d8bb6dae29","order_by":1,"name":"Shujian Chen","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Shujian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-05-15 23:39:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6675902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6675902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83008556,"identity":"0b1cb126-c498-4ed8-bdd9-696331570b62","added_by":"auto","created_at":"2025-05-19 04:08:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":591311,"visible":true,"origin":"","legend":"","description":"","filename":"4a68d57c309b46dc819c95efea199a19.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6675902/v1_covered_2564433d-4ba8-421d-939d-361c68606e67.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMaestro: Multi-Agent Enhanced System for Task Recognition and Optimization in Manufacturing Lines\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Carnegie Mellon University","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":"Neural Visualization, Human Center Interaction, User Feedback","lastPublishedDoi":"10.21203/rs.3.rs-6675902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6675902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManufacturing lines face numerous challenges in task recognition and optimization, particularly due to their dynamic nature. 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