Predictive Modeling for Sortation and Delivery Optimization in E-Commerce Logistics

Predictive Modeling for Sortation and Delivery Optimization in E-Commerce Logistics | 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 Predictive Modeling for Sortation and Delivery Optimization in E-Commerce Logistics Junbo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6150011/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 E-commerce logistics faces ongoing challenges in managing sortation and delivery processes efficiently. The complexity of order volumes, varying delivery times, and logistical constraints necessitates advanced predictive modeling techniques. To address these challenges, we introduce a comprehensive framework that incorporates machine learning algorithms to analyze historical shipping data and real-time information. By uncovering patterns associated with demand and operational dynamics, our model forecasts peak periods and optimizes the sorting and delivery processes. Additional simulations and real-world experiments validate our approach, revealing enhanced delivery speed, improved resource allocation, and reduced operational costs in comparison to conventional logistics models. Our framework signifies a step forward in leveraging predictive modeling to elevate e-commerce logistics, ultimately aiming for enhanced performance and heightened customer satisfaction. Computer Architecture and Engineering E-commerce Logistics Resource Allocation 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. 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-6150011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423673889,"identity":"26818dbd-4b1b-421c-a2c8-3319aa8013ba","order_by":0,"name":"Junbo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgYGNiBlgyoqQYSWNFRFeLUAAUjLYRK0MM/IPfbgY9v5On7p3oOPKxjsog0OMB+8zYPPYTPy0g1ntt2WkJxzLtnwDENy7oYDbMnW+LXkmEnzbrstYXAjx0yygeEAUAuPmTQRWs5J2N/IMf8J0cL/jRgtByQMJHLMGKG2sOHX0vPG3HDmv2TJGXfOJUs2GCTnzjzMZmw5B48Ww/Ycswcfztjx88/uPfixocIut+9488Mbb/BpaYCxJECOMQBiZjzKQUAezpLA5/5RMApGwSgY0QAAwrNKpAkSZvgAAAAASUVORK5CYII=","orcid":"","institution":"Carnegie Mellon University","correspondingAuthor":true,"prefix":"","firstName":"Junbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-04 02:12:54","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6150011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6150011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77838302,"identity":"5254d856-2920-4610-b7fa-a5422aae4b0b","added_by":"auto","created_at":"2025-03-06 04:12:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":526276,"visible":true,"origin":"","legend":"","description":"","filename":"PredictiveModelingforSortationandDeliveryOptimizationinECommerceLogistics2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6150011/v1_covered_bd0591b3-765b-4b1e-a5f7-8edcae564392.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePredictive Modeling for Sortation and Delivery Optimization in E-Commerce Logistics\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Carnegie Mellon 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":"E-commerce Logistics, Resource Allocation","lastPublishedDoi":"10.21203/rs.3.rs-6150011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6150011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eE-commerce logistics faces ongoing challenges in managing sortation and delivery processes efficiently. The complexity of order volumes, varying delivery times, and logistical constraints necessitates advanced predictive modeling techniques. To address these challenges, we introduce a comprehensive framework that incorporates machine learning algorithms to analyze historical shipping data and real-time information. By uncovering patterns associated with demand and operational dynamics, our model forecasts peak periods and optimizes the sorting and delivery processes. Additional simulations and real-world experiments validate our approach, revealing enhanced delivery speed, improved resource allocation, and reduced operational costs in comparison to conventional logistics models. Our framework signifies a step forward in leveraging predictive modeling to elevate e-commerce logistics, ultimately aiming for enhanced performance and heightened customer satisfaction.\u003c/p\u003e","manuscriptTitle":"Predictive Modeling for Sortation and Delivery Optimization in E-Commerce Logistics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-06 04:04:12","doi":"10.21203/rs.3.rs-6150011/v1","editorialEvents":[{"type":"communityComments","content":2}],"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":"b55a4754-7fb6-4fa0-99ec-b0850a38e7cb","owner":[],"postedDate":"March 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45148344,"name":"Computer Architecture and Engineering"}],"tags":[],"updatedAt":"2025-03-06T04:04:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-06 04:04:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6150011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6150011","identity":"rs-6150011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}