Maximizing Operational Flow in Multi-Component Processing

Maximizing Operational Flow in Multi-Component Processing | 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Research Article Maximizing Operational Flow in Multi-Component Processing Abhijit Gaikwad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8658842/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 This industrial engineering paper addresses the challenge of optimizing concurrent production within multi-component processing environments. It presents a comprehensive analytical approach to maximize the overall operational flow, considering various product characteristics and processing constraints. The work details the mathematical modeling and solution methodologies employed to understand and control the intricate interactions that govern system throughput. This project effectively models the inherent feedback loops within batch operations, demonstrating how precise parameter adjustments drive performance maximization. The findings offer practical insights for enhancing productivity and efficiency in complex manufacturing and production systems. Batch Scheduling Genetic Algorithm Multi-Product Manufacturing Makespan Minimization Machine Utilization Evolutionary Optimization Discrete Event Simulation Production Planning Smart Manufacturing Scheduling Heuristics Resource Allocation Performance Evaluation Simulation Modeling Industrial Automation Job Shop Scheduling Metaheuristic Algorithms 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. 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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-8658842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578057709,"identity":"cdca79ed-9d0c-4e62-b050-dc9fd63d8121","order_by":0,"name":"Abhijit Gaikwad","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Abhijit","middleName":"","lastName":"Gaikwad","suffix":""}],"badges":[],"createdAt":"2026-01-21 11:20:35","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-8658842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8658842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100846166,"identity":"c290f549-25ad-4f3a-a70f-87e26f228512","added_by":"auto","created_at":"2026-01-22 04:26:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":180470,"visible":true,"origin":"","legend":"","description":"","filename":"MaximizingOperationalFlowinMultiComponentProcessing1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8658842/v1_covered_e7458a40-6d13-4456-bc03-348eeedaafbd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMaximizing Operational Flow in Multi-Component Processing\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lamar 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":"Batch Scheduling, Genetic Algorithm, Multi-Product Manufacturing, Makespan Minimization, Machine Utilization, Evolutionary Optimization, Discrete Event Simulation, Production Planning, Smart Manufacturing, Scheduling Heuristics, Resource Allocation, Performance Evaluation, Simulation Modeling, Industrial Automation, Job Shop Scheduling, Metaheuristic Algorithms","lastPublishedDoi":"10.21203/rs.3.rs-8658842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8658842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis industrial engineering paper addresses the challenge of optimizing concurrent production within multi-component processing environments. 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