Genetic Algorithm for Multi-Warehouse Item Allocation and Routing

Genetic Algorithm for Multi-Warehouse Item Allocation and Routing | 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 Method Article Genetic Algorithm for Multi-Warehouse Item Allocation and Routing Miracle Kwabla Lassey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7402248/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract This study introduces a genetic algorithm-based model designed to optimize item delivery within complex logistics networks involving multiple warehouses and demand centers, all constrained by specific delivery time windows. Unlike traditional routing approaches, the proposed framework em phasizes the grouping of items prior to vehicle assignment, aiming to minimize total transportation costs. The model incorporates vehicle type selection and item grouping, enabling more efficient fleet utilization and route planning. To enhance performance and robustness, the algorithm parameters are calibrated using manual parameter calibration and sensitivity analysis methods. The results demonstrate the model’s capacity to identify cost-effective delivery strategies, highlighting its poten tial applicability to real-world logistics operations seeking operational cost reductions and improved delivery efficiency. Operations Research Genetic algorithm VRP Delivery cost Vehicle assignment Item Clustering Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-7402248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":502709639,"identity":"d28f0dfe-d4cc-4f00-8c99-ce600cb8a605","order_by":0,"name":"Miracle Kwabla Lassey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACZhBRwCAD4VWARJgbiNBiwMAD4Z0BiTAS0MKArIWxDUzi16Lbzvvwww8DGx7+/uMPH/POq43mbwdq+VGxDacWs8PsxpI9Bmk8EjdyjI15tx3PnXGYsYGx58xtPFrYGKQZDA7zMNzgYZPm3XYstwGohZmxDa8W5t8MBv955M8ff/6bd86x3PlEaGED2nKAx+BAghkzb0NN7gZitFj2GCTzGAL9Ijnn2IHcjUAtB/H65fwx5hs/Kuzk5M4ff/jhTU1d7rzzhw8++FGBWwsKYOJhOAxmHCBOPRAw/mCoI1rxKBgFo2AUjBwAAPD8VkOPnv7YAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-6605-6540","institution":"Abdullah Gül Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"Miracle","middleName":"Kwabla","lastName":"Lassey","suffix":""}],"badges":[],"createdAt":"2025-08-18 18:32:52","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7402248/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-7402248/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94223833,"identity":"a686017c-717d-4533-ac04-ccf0657d106a","added_by":"auto","created_at":"2025-10-23 19:11:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":577650,"visible":true,"origin":"","legend":"","description":"","filename":"GAforMultiWarehouseItemAllocationandRouting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7402248/v2_covered_6d53a131-64c1-4872-8ab0-52b59f505921.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGenetic Algorithm for Multi-Warehouse Item Allocation and Routing\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Genetic algorithm, VRP, Delivery cost, Vehicle assignment, Item Clustering","lastPublishedDoi":"10.21203/rs.3.rs-7402248/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7402248/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study introduces a genetic algorithm-based model designed to optimize item delivery within \u0026nbsp;complex logistics networks involving multiple warehouses and demand centers, all constrained by \u0026nbsp;specific delivery time windows. Unlike traditional routing approaches, the proposed framework em phasizes the grouping of items prior to vehicle assignment, aiming to minimize total transportation \u0026nbsp;costs. The model incorporates vehicle type selection and item grouping, enabling more efficient fleet \u0026nbsp;utilization and route planning. To enhance performance and robustness, the algorithm parameters \u0026nbsp;are calibrated using manual parameter calibration and sensitivity analysis methods. The results \u0026nbsp;demonstrate the model’s capacity to identify cost-effective delivery strategies, highlighting its poten tial applicability to real-world logistics operations seeking operational cost reductions and improved \u0026nbsp;delivery efficiency.\u003c/p\u003e","manuscriptTitle":"Genetic Algorithm for Multi-Warehouse Item Allocation and Routing","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-10-23 18:54:56","doi":"10.21203/rs.3.rs-7402248/v2","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}},{"code":1,"date":"2025-08-19 17:36:48","doi":"10.21203/rs.3.rs-7402248/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":"830a5809-7d0c-465e-b64a-a161a04938fc","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56301896,"name":"Operations Research"}],"tags":[],"updatedAt":"2025-08-19T17:36:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 18:54:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-7402248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7402248","identity":"rs-7402248","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}