Discrete event simulation-driven digital twin method for solving permutation flowshop scheduling problems

Discrete event simulation-driven digital twin method for solving permutation flowshop scheduling problems | 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 Discrete event simulation-driven digital twin method for solving permutation flowshop scheduling problems Guangzhen Li, Lei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3856998/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 Digital twin technology is becoming increasingly important in intelligent manufacturing and Industry 4.0. However, the simulation system of a digital twin often only displays a virtual version of the reality system, ignoring its potential for strong analysis and decision-making abilities. This paper presents a discrete event simulation-driven digital twin method to solve the permutation flowshop scheduling problem. In permutation flowshop scheduling, the concept of recovery time is proposed, which is often ignored. To address this issue, we propose a discrete event simulation-driven method to solve the PFSP with SDRT. The computational results demonstrate the effectiveness of the simulation-based optimization methodology. It is important to consider recovery time alone as a constraint, which can improve scheduling performance by about 14% compared to merging recovery time into the processing time in traditional mathematical modeling and optimization. The results show the feasibility and advantages of simulation system analysis and decision-making in digital twin applications. Physical sciences/Engineering Physical sciences/Engineering/Mechanical engineering Digital twin discrete event simulation flowshop sequence-dependent recovery times 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-3856998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":270799696,"identity":"636ae914-0a7c-4433-b185-e1cd457e7670","order_by":0,"name":"Guangzhen Li","email":"","orcid":"","institution":"Chinese Academy of Science","correspondingAuthor":false,"prefix":"","firstName":"Guangzhen","middleName":"","lastName":"Li","suffix":""},{"id":270799697,"identity":"9c060ee8-6eaa-4e98-af82-6de2b512dfa6","order_by":1,"name":"Lei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACNvbmgw8+/GBj5mfvf/ggoaKGsBY+nmPJhjN7+Ngle84wGzw4c4ywFjmJHDVpDjY5foMbOWySD1uYiXAYzxlmYwYeM2mGM2ePVSQ2sDHwt3cnEPBL78HHBRZpxoztfWk3EnfIMEicObuBgC3nko1nAD3EzHPA7EbiGTYGA4lcAlokcsykedj+17dJJJgVJLYxE62FjZkHyGAgTgskkNmYJYAMiYQzx3gI+kW+HRqV9sebD378UVEjx9/ei18LBuAhTfkoGAWjYBSMAqwAAOeSSTEmNSUhAAAAAElFTkSuQmCC","orcid":"","institution":"Chinese Academy of Science","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-12 12:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3856998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3856998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51417084,"identity":"e87663f8-00d9-4f34-bea6-6fc3fcb1da77","added_by":"auto","created_at":"2024-02-21 07:57:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":384047,"visible":true,"origin":"","legend":"","description":"","filename":"SR20240103Discreteeventsimulationdrivenmethodsolvingpermutationflowshopschedulingproblemindigitaltwins.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3856998/v1_covered_7b734a98-6866-47b4-aa95-cc563361dda2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discrete event simulation-driven digital twin method for solving permutation flowshop scheduling problems","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":"Digital twin, discrete event simulation, flowshop, sequence-dependent recovery times","lastPublishedDoi":"10.21203/rs.3.rs-3856998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3856998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Digital twin technology is becoming increasingly important in intelligent manufacturing and Industry 4.0. 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