An Optimization Approach for Blood Supply Chain Management Integrating Drone Delivery Method

An Optimization Approach for Blood Supply Chain Management Integrating Drone Delivery Method | 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 An Optimization Approach for Blood Supply Chain Management Integrating Drone Delivery Method Mayuri K N, Gowsalya M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5614125/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This paper investigates the application of drone delivery for blood supply chainmanagement (BSCM). This paper aims to minimize the delivery lead time andmake the fastest delivery of lifesaving blood products within a specified deliveryrange. We proposed an optimization model that utilizes Mixed Integer Non-LinearProgramming (MINLP) with Dijkstra’s algorithm. This model considers thedrone capabilities such as payload capacity, travel speed, and range. This studyinvestigates the possibilities of drone delivery in blood supply chain management,with a priority on reducing delivery lead time and enabling rapid deliveries. Whileincorporating real-world scenarios with multiple locations like hospitals, clinics,and demand location. Randomly generated test instances are used. In order toevaluate the model’s effectiveness, Numerical illustration has been analysed usingPython programming as the development platform for implementing the solution. Blood supply chain Drone delivery Optimization MINLP Shortest route Time minimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Feb, 2025 Reviews received at journal 03 Feb, 2025 Reviewers agreed at journal 17 Jan, 2025 Reviews received at journal 16 Jan, 2025 Reviewers agreed at journal 15 Jan, 2025 Reviewers agreed at journal 09 Jan, 2025 Reviews received at journal 03 Jan, 2025 Reviewers agreed at journal 24 Dec, 2024 Reviewers invited by journal 24 Dec, 2024 Editor assigned by journal 23 Dec, 2024 Submission checks completed at journal 17 Dec, 2024 First submitted to journal 10 Dec, 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. 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-5614125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391389961,"identity":"e97aac5f-85fb-4730-8587-49915386857d","order_by":0,"name":"Mayuri K N","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Mayuri","middleName":"K","lastName":"N","suffix":""},{"id":391389962,"identity":"bb09c83a-d2d1-482d-b761-9162dffa33a5","order_by":1,"name":"Gowsalya M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYNCCAhDBfIAhAcxjbCBCiwGIYEtgSEggTQsPkEwgQrG8e4/xhx8Gh+UZxM58k3j4g0Gev4G57QE+LYZnzphJ9hgcNmyQzt0mAXSY4YwDjO0GeLXMyN0GdNJhRqCWzQZALYwbGBjbJAho2fzxj8Fh+wbpnMcgLfYEtchL5G6QBtqSCNTC+ACoJZGgFgOe89+kZQzSk9uk0wwfJKRJJM84TMiW9rbkj28qrG37pZMfHPxhY2Pb397+DL8tB8BUMwMbhA9UzIxPPciWBjBVR0DZKBgFo2AUjGgAAOI8RVwWWIsjAAAAAElFTkSuQmCC","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Gowsalya","middleName":"","lastName":"M","suffix":""}],"badges":[],"createdAt":"2024-12-10 07:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5614125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5614125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71828929,"identity":"f5b92df8-e5a6-4b4f-a163-0d469efcd1ad","added_by":"auto","created_at":"2024-12-19 02:13:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432698,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchSpringerNatureLaTeXTemplate1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5614125/v1_covered_218491a9-ec2d-45a5-b43c-0e467ed4bf5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Optimization Approach for Blood Supply Chain Management Integrating Drone Delivery Method","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-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blood supply chain, Drone delivery, Optimization, MINLP, Shortest route, Time minimization","lastPublishedDoi":"10.21203/rs.3.rs-5614125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5614125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper investigates the application of drone delivery for blood supply chainmanagement (BSCM). This paper aims to minimize the delivery lead time andmake the fastest delivery of lifesaving blood products within a specified deliveryrange. We proposed an optimization model that utilizes Mixed Integer Non-LinearProgramming (MINLP) with Dijkstra’s algorithm. This model considers thedrone capabilities such as payload capacity, travel speed, and range. This studyinvestigates the possibilities of drone delivery in blood supply chain management,with a priority on reducing delivery lead time and enabling rapid deliveries. Whileincorporating real-world scenarios with multiple locations like hospitals, clinics,and demand location. Randomly generated test instances are used. In order toevaluate the model’s effectiveness, Numerical illustration has been analysed usingPython programming as the development platform for implementing the solution.","manuscriptTitle":"An Optimization Approach for Blood Supply Chain Management Integrating Drone Delivery Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 02:05:37","doi":"10.21203/rs.3.rs-5614125/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-05T14:23:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-03T09:37:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89487509519192633789623241726550613356","date":"2025-01-17T16:11:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-16T17:24:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175093622614043048220722558905773739422","date":"2025-01-15T17:28:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104397054793792501680610871832636992911","date":"2025-01-09T11:37:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-04T02:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325901407407521748532688206887544870210","date":"2024-12-24T06:26:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-24T05:00:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-23T15:11:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-17T12:07:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2024-12-10T07:24:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"729d3c6a-4829-403b-bc8e-6898c80a2218","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-18T05:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-19 02:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5614125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5614125","identity":"rs-5614125","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}