AI-Powered Evolutionary Algorithm for Optimizing Truck Scheduling in Multi-Dock Truck Cross-Docking Centers

AI-Powered Evolutionary Algorithm for Optimizing Truck Scheduling in Multi-Dock Truck Cross-Docking Centers | 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 AI-Powered Evolutionary Algorithm for Optimizing Truck Scheduling in Multi-Dock Truck Cross-Docking Centers Thiago Henrique Nogueira, Felipe Provezano Coutinho, Maria Gabriela Mendonça Peixoto, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4376918/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Evolutionary Intelligence → Version 1 posted 11 You are reading this latest preprint version Abstract Fast and adaptive logistics are essential for meeting evolving customer expectations and needs. In response to recent shifts in consumer behavior, which have altered the perceived value of products, companies, and services, organizations are increasingly turning to intelligent algorithms to swiftly adapt to various scenarios. Cross-docking, a strategy known for enhancing flexibility and reducing delivery times, is central to this approach. This paper introduces an advanced AI-driven framework utilizing an evolutionary algorithm to tackle the multi-dock truck sequencing challenge in cross-docking centers, aimed at minimizing makespan. Our method includes a machine learning-based parameter tuning strategy, enabling the algorithm to automatically adjust to different instances without human intervention. We evaluated our framework by comparing results obtained with and without machine learning tuning against state-of-the-art methods. The findings indicate that our AI-enhanced approach not only delivers superior feasible solutions but also achieves these results with reduced computational times, positioning it as a highly effective tool for logistics optimization. Truck Scheduling Cross-docking Evolutionary Algorithm Logistics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Evolutionary Intelligence → Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviews received at journal 26 Jun, 2024 Reviews received at journal 17 Jun, 2024 Reviewers agreed at journal 09 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers invited by journal 08 Jun, 2024 Submission checks completed at journal 07 May, 2024 Editor assigned by journal 07 May, 2024 First submitted to journal 06 May, 2024 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. 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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-4376918","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301542381,"identity":"407cf503-dae4-467d-984f-ee5f9d303b93","order_by":0,"name":"Thiago Henrique Nogueira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCcYGIHkAyjNgYOBnYHzAABbEAXgwtEg2MBsQ0AKmDiBEDA4Q0GIv3dz48UvFHQZz9vZrEh8K7skb30hmk2DccQ+3LTIHm6VlzjxjsOw5UyY5w6DYcBtYy5liPA5LbJCWbDvMYHAjJ02axyCBcduN/MMGjG0J+LQ0/4Zr+WOQYL95RjIzIS1tkh/BWtKPSTMYJCRukEhmfIBXy43ENmuGM4d5DM6cYbbsMUhInnHmMeODxDO4tbDPSH9880fFYTmD4+0Pb/z4k2Db357McODjDtxaQICZB2gbEBkghPBrYGBg/AGx8AEBdaNgFIyCUTBSAQDP21hWGtrrawAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":true,"prefix":"","firstName":"Thiago","middleName":"Henrique","lastName":"Nogueira","suffix":""},{"id":301542382,"identity":"f7448649-0132-417c-b36a-19202f52d81b","order_by":1,"name":"Felipe Provezano Coutinho","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"Provezano","lastName":"Coutinho","suffix":""},{"id":301542383,"identity":"c2bdde0a-2704-4fd8-87d8-76593c3705cb","order_by":2,"name":"Maria Gabriela Mendonça Peixoto","email":"","orcid":"","institution":"University of Brasília","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Gabriela Mendonça","lastName":"Peixoto","suffix":""},{"id":301542384,"identity":"79d1249d-f95a-4f66-b2e1-bbf3a3e24fae","order_by":3,"name":"Eduardo Gontijo Carrano","email":"","orcid":"","institution":"Universidade Federal de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"Gontijo","lastName":"Carrano","suffix":""},{"id":301542385,"identity":"4874e0b3-c5c6-4716-b3e4-ed6654731595","order_by":4,"name":"Martín Gómez Ravetti","email":"","orcid":"","institution":"Universidade Federal de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Martín","middleName":"Gómez","lastName":"Ravetti","suffix":""}],"badges":[],"createdAt":"2024-05-06 12:47:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4376918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4376918/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12065-024-00992-x","type":"published","date":"2024-11-15T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69274497,"identity":"4ed8f97d-5e20-47d6-8936-a9ca56e86d95","added_by":"auto","created_at":"2024-11-18 16:01:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":542400,"visible":true,"origin":"","legend":"","description":"","filename":"OPTLettersv35.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4376918/v1_covered_6f83a97a-e4ae-4df6-bb2e-3669a9c1ef5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Powered Evolutionary Algorithm for Optimizing Truck Scheduling in Multi-Dock Truck Cross-Docking Centers","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"evolutionary-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evin","sideBox":"Learn more about [Evolutionary Intelligence](http://link.springer.com/journal/12065)","snPcode":"12065","submissionUrl":"https://submission.nature.com/new-submission/12065/3","title":"Evolutionary Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Truck, Scheduling, Cross-docking, Evolutionary Algorithm, Logistics","lastPublishedDoi":"10.21203/rs.3.rs-4376918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4376918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Fast and adaptive logistics are essential for meeting evolving customer expectations and needs. 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