Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions

Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions | 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 Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions Jia Ma, Xinru Ma, Chulian Li, Tongyan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4711383/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Discover Computing → Version 1 posted 11 You are reading this latest preprint version Abstract Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced. vehicle routing problem with drones convolution neural network model hybrid genetic algorithm large-scale neighborhood search Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 20 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviews received at journal 13 Aug, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers invited by journal 24 Jul, 2024 Editor assigned by journal 22 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 09 Jul, 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-4711383","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337671688,"identity":"ba328716-801b-4113-b939-e1a4e52584a1","order_by":0,"name":"Jia Ma","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Ma","suffix":""},{"id":337671689,"identity":"89c1dd03-978b-4d3b-9542-507eefa630ba","order_by":1,"name":"Xinru Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCA0DMw48kYECUFhnJBmYStICAjcEBYrXotp8xPFy45zCP8fnzxyQY/tglNrA3b5NgqLmDU4vZmbSEwzOeHeYxu5HMJsHYlpzYwHOsTILh2DPcWg4kHzjMcwCkhRmopYE5sUEixwzIOIxby/mHDWAtxv2H2YAOq09skH9DQMsNqC0GDECHMbAdBtrCQ0jLswSglnQeiRvJxhaJbceN23jSii0SjuFzWI7xZ54D1vb8/Qcf3vjwp1q2n/3wxhsfanBrQQUJQMwGY4yCUTAKRsEoIB8AABRLUy23z64gAAAAAElFTkSuQmCC","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":true,"prefix":"","firstName":"Xinru","middleName":"","lastName":"Ma","suffix":""},{"id":337671690,"identity":"c1f0b19d-6c0c-4c76-a9e5-503855171a2a","order_by":2,"name":"Chulian Li","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Chulian","middleName":"","lastName":"Li","suffix":""},{"id":337671691,"identity":"ebd0922c-0bae-41b1-847f-aee20cfb4e43","order_by":3,"name":"Tongyan Li","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Tongyan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-09 10:45:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4711383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4711383/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10791-024-09469-y","type":"published","date":"2024-11-11T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69286835,"identity":"62b20c03-0d77-46dd-ade3-a37532ed4814","added_by":"auto","created_at":"2024-11-18 19:40:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":958008,"visible":true,"origin":"","legend":"","description":"","filename":"Vehicledronecollaborativedistributionpathplanningbasedonneuralarchitecturesearchundertheinfluenceofcarbonemissions.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4711383/v1_covered_5784c0d9-3ec7-4247-8209-eb6fdb4a3369.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"vehicle routing problem with drones, convolution neural network model;hybrid genetic algorithm, large-scale neighborhood search, ","lastPublishedDoi":"10.21203/rs.3.rs-4711383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4711383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. 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