Application of a new assignment algorithm based on the minimax difference in earthquake emergency rescue

Application of a new assignment algorithm based on the minimax difference in earthquake emergency rescue | 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 Application of a new assignment algorithm based on the minimax difference in earthquake emergency rescue Sining Huang, Ying Zhang, Tiantian Qiao, Junwu Dai, Jinlong Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742278/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The primary challenge in earthquake emergency response is the effective dispatch of large-scale rescue teams to disaster areas after an earthquake disaster occurs, especially as this often involves time constraints and road network interruptions. To solve this, we propose a minimax difference submatrix (MDS) algorithm combined with k-means clustering. First, the k-means method is utilized to cluster the rescue locations with similar features, thereby transforming the non-standard assignment problem (rescue team $\neq$ rescue location) into a solvable linear assignment problem (LAP). After obtaining the cost matrix, the principle of minimax difference is established; that is, the columns with the largest differences between the maximum and minimum values are selected in sequence for priority assignment. By comparing the results of numerical experiments, it can be seen that the MDS method reduces the computational load effectively compared with the Hungarian algorithm, and can obtain a high-quality approximate optimal solution for the linear assignment problem. Finally, through the case study of simulating the IX-X earthquake intensity scenario in City T, it is demonstrated that the MDS method is an efficient and stable approach for solving the problem of large-scale real-time rescue in earthquake emergencies. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Earthquake emergency rescue Rescue team scheduling Linear assignment problem K-means clustering Minimax difference submatrix (MDS) Full Text Additional Declarations No competing interests reported. Supplementary Files DeclarationofInterestStatement.docx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Editor invited by journal 11 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 10 Feb, 2026 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-8742278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591499762,"identity":"d8c8348c-578b-49fb-99a8-3404000e1b17","order_by":0,"name":"Sining Huang","email":"","orcid":"","institution":"China University of Petroleum (East China)","correspondingAuthor":false,"prefix":"","firstName":"Sining","middleName":"","lastName":"Huang","suffix":""},{"id":591499763,"identity":"307c4ffc-aa7a-46f6-9de4-6bb802273e5e","order_by":1,"name":"Ying Zhang","email":"","orcid":"","institution":"China University of Petroleum (East 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