Research on dynamic distributed flow shop dynamic scheduling based on LSTM-PPO

Research on dynamic distributed flow shop dynamic scheduling based on LSTM-PPO | 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 Research on dynamic distributed flow shop dynamic scheduling based on LSTM-PPO Chuchu Rao, Renwang LI, Yeshen Lan, Junxian Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5264638/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 Aiming at the Dynamic Distributed Flow Shop Scheduling Problem (DDFSP), a scheduling solution method combining a Long short-term memory (LSTM) network with a Proximal Policy Optimization(PPO) algorithm is proposed. To solve the scheduling confusion caused by many uncertain factors in the scheduling process. By learning and analyzing a large amount of data in the dynamic distributed workshop system, the approximate optimal solution is obtained by integrating the time sequence feature extraction capability of the LSTM network and the optimization efficiency of the PPO algorithm, to realize more intelligent and flexible scheduling decision-making, improve production efficiency and process optimization level, and verified by experiment and simulation. The results show that this method can improve production efficiency and resource utilization, and has the potential for cost control. DDFSP LSTM PPO Dynamic state Production efficiency 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-5264638","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369414570,"identity":"0a88fe59-7195-4915-b713-d5500a67d82a","order_by":0,"name":"Chuchu Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYJACZiBOYGBvbHz4gTQtPIebjSVI0yKR3ibAQ4xy8/bDBz8XttnlGdx82MYgwWAnp9tAQIvMmbRk6ZltycUGtxPbHhQwJBubHSCgRYIhx4yZt+1A4obbie0GEgwHErcR1ML/Bqrl5sE2CR6itEjAbLnBSLSWZ8nSPOeSE2eeSQQGsgExfuFPPviZp8wuse/48YcPP1TYyRHUggYMSFM+CkbBKBgFowAHAAAJ9EEEpMGa+wAAAABJRU5ErkJggg==","orcid":"","institution":"Quzhou College of Technical","correspondingAuthor":true,"prefix":"","firstName":"Chuchu","middleName":"","lastName":"Rao","suffix":""},{"id":369414571,"identity":"7a43ddbb-2eff-4c61-9730-44382f51ed4a","order_by":1,"name":"Renwang LI","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Renwang","middleName":"","lastName":"LI","suffix":""},{"id":369414572,"identity":"f0415512-f90a-4032-a822-0e4bb26549f7","order_by":2,"name":"Yeshen Lan","email":"","orcid":"","institution":"Quzhou College of Technical","correspondingAuthor":false,"prefix":"","firstName":"Yeshen","middleName":"","lastName":"Lan","suffix":""},{"id":369414573,"identity":"a38923d2-637f-4330-b8bb-c7410673a0b1","order_by":3,"name":"Junxian Chen","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junxian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-10-15 03:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5264638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5264638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69114950,"identity":"53230e74-6b67-46fe-8284-52ec11eb4b10","added_by":"auto","created_at":"2024-11-15 21:46:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":982176,"visible":true,"origin":"","legend":"","description":"","filename":"submit120241022223102.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5264638/v1_covered_1636ea9c-c69a-44fe-a40d-0ff799d97b46.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on dynamic distributed flow shop dynamic scheduling based on LSTM-PPO","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":"DDFSP,LSTM,PPO,Dynamic state,Production efficiency","lastPublishedDoi":"10.21203/rs.3.rs-5264638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5264638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Aiming at the Dynamic Distributed Flow Shop Scheduling Problem (DDFSP), a scheduling solution method combining a Long short-term memory (LSTM) network with a Proximal Policy Optimization(PPO) algorithm is proposed. 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