Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming

Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming | 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 Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming Jiangao Zhang, Suyin Cui, Pei Zhu, Liya Wang, Zhi Sun, Quan Shao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8221874/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 Drone logistics transportation is difficult to achieve long-distance delivery directly due to the short range of drones, and reasonable vertiport location and air-route planning have become key issues that need to be urgently addressed. This study proposes a bi-layer optimization approach to address the problems of vertiport location and air-route planning, investigating the interaction between facility placement and route design. Considering the variations in coverage demand, the proposed methodology incorporates a hierarchical vertiport construction strategy and establishes a multi-tier delivery system utilizing heterogeneous drone fleets. The improved GA and GACO are used to solve the site selection and path planning models, respectively. The benchmark and case study results show that the improved GA and GACO algorithm are better than the original algorithm in terms of solution quality and coverage in the same scenario and benchmark. The improved GACO algorithm provide better average results, higher stability, and effectively reducing flight time and path costs. The proposed algorithms achieve a total cost reduction of 8.96%. The results further verify that bi-layer programming approach maintains excellent cost optimization performance and network adaptability even as the number of demand points increases. In addition, the hierarchical vertiport construction strategy generates 6.51%-11.65% cost savings compared to conventional non-hierarchical approaches. The proposed delivery network exhibits strong robustness and has good adaptability to the influence of data noise. This study not only fills a theoretical gap in the cooperative planning of vertiports and routes, but also provides critical technical support for the scalable implementation of drone logistics, offering significant theoretical value and practical engineering implications. Physical sciences/Engineering Physical sciences/Mathematics and computing location-routing problem bi-layer model genetic algorithm ant colony algorithm logistics drone Full Text Additional Declarations No competing interests reported. Supplementary Files file.xlsx 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-8221874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558246710,"identity":"cdc2a8e5-8858-4872-bdd6-371a65af2eb6","order_by":0,"name":"Jiangao 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Programming","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":"location-routing problem, bi-layer model, genetic algorithm, ant colony algorithm, logistics 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This study proposes a bi-layer optimization approach to address the problems of vertiport location and air-route planning, investigating the interaction between facility placement and route design. Considering the variations in coverage demand, the proposed methodology incorporates a hierarchical vertiport construction strategy and establishes a multi-tier delivery system utilizing heterogeneous drone fleets. The improved GA and GACO are used to solve the site selection and path planning models, respectively. The benchmark and case study results show that the improved GA and GACO algorithm are better than the original algorithm in terms of solution quality and coverage in the same scenario and benchmark. The improved GACO algorithm provide better average results, higher stability, and effectively reducing flight time and path costs. The proposed algorithms achieve a total cost reduction of 8.96%. The results further verify that bi-layer programming approach maintains excellent cost optimization performance and network adaptability even as the number of demand points increases. In addition, the hierarchical vertiport construction strategy generates 6.51%-11.65% cost savings compared to conventional non-hierarchical approaches. The proposed delivery network exhibits strong robustness and has good adaptability to the influence of data noise. This study not only fills a theoretical gap in the cooperative planning of vertiports and routes, but also provides critical technical support for the scalable implementation of drone logistics, offering significant theoretical value and practical engineering implications.\u003c/p\u003e","manuscriptTitle":"Research on Location-Routing Problem for Logistics UAVs Based on Bi-layer Programming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 08:07:36","doi":"10.21203/rs.3.rs-8221874/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9a7d09e8-635f-4c57-85ed-b1e254bc5fe0","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59422799,"name":"Physical sciences/Engineering"},{"id":59422800,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-24T22:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 08:07:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8221874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8221874","identity":"rs-8221874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}