Design of Marine Oil Spill Emergency Resource Scheduling Framework Based on Improved A*-PSO-SA-BPPO

Design of Marine Oil Spill Emergency Resource Scheduling Framework Based on Improved A*-PSO-SA-BPPO | 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 Design of Marine Oil Spill Emergency Resource Scheduling Framework Based on Improved A*-PSO-SA-BPPO Jihao An, Peng Ren, Xinrong Lyu, Christos Grecos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9253567/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Marine oil spills cause severe damage to ocean ecosystems and coastal economies. Timely scheduling of oil spill emergency resources is crucial for effective emergency response. Addressing the challenge of efficient resource scheduling under dynamically evolving oil spill conditions in marine environments, this paper proposes a hierarchical hybrid intelligent architecture. Firstly, it integrates an improved A*-PSO path planning module and an attention-based Bayesian Proximal Policy Optimization (SA-BPPO) reinforcement learning module to construct a 'path navigation-decision optimization' model for oil spill emergency resource scheduling. The improved A*-PSO path planning module generates initial paths and performs smoothing optimization, enabling rapid planning of efficient paths from start to end points, enhancing their feasibility and safety. Secondly, the self-attention Bayesian PPO (SA-BPPO) reinforcement learning module focuses on optimizing ship cleaning strategies. By incorporating an attention mechanism, the model’s ability to focus on key information is enhanced. The Bayesian method estimates the uncertainty of strategy outputs, allowing ships to dynamically adjust cleaning strategies according to environmental changes. Experimental results demonstrate that the proposed architecture significantly outperforms algorithms like Deep Q-Learning (DQN), Soft Actor-Critic (SAC), Ant Colony Optimization (ACO), and Dijkstra in key indicators such as cleaning time, efficiency, and path optimization. Compared to DQN, the improved A*-PSO-SA-BPPO algorithm improves average cleaning efficiency by 36%. Compared to SAC, it improves average cleaning efficiency by 9.17%. Compared to ACO and Dijkstra algorithms, it improves average cleaning efficiency by 36.78% and 75%, respectively. Ablation experiments further validate the effectiveness of each module that adding the improved A*-PSO module reduces cleaning time by 17.24% and increases cleaning efficiency by 39.45%. Marine oil spill Emergency resource scheduling PSO PPO Path planning Bayesian estimation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 29 Mar, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 28 Mar, 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-9253567","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624894111,"identity":"ac91d3f7-77d9-4086-9425-5e7ba06de7f3","order_by":0,"name":"Jihao An","email":"","orcid":"","institution":"China University of Petroleum, East China","correspondingAuthor":false,"prefix":"","firstName":"Jihao","middleName":"","lastName":"An","suffix":""},{"id":624894112,"identity":"a1239534-004c-4253-af18-024fb79ac633","order_by":1,"name":"Peng Ren","email":"","orcid":"","institution":"China University of Petroleum, East China","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Ren","suffix":""},{"id":624894115,"identity":"b47f8314-d727-47d2-a568-14fd0803db17","order_by":2,"name":"Xinrong Lyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYBACPhDxAcI2IE4LGxAzziBZCzMPaVokcsw+27bdkWdgb94mwVBzhxgtacmzc9ueGTbwHCuTYDj2jAgtPIcPM+e2HU5gAFonwdhwmBgtB5uZLUFa5N8Qq4W9+TAzI9gWHqK1tCUz9px7ZtjGk1ZskXCMCC38zDzGDD/K7sjzsx/eeONDDRFaoOAAOIIYEojWANIyCkbBKBgFowAnAACVES/2UTy1kQAAAABJRU5ErkJggg==","orcid":"","institution":"China University of Petroleum, East China","correspondingAuthor":true,"prefix":"","firstName":"Xinrong","middleName":"","lastName":"Lyu","suffix":""},{"id":624894116,"identity":"58f4a316-2749-4f2e-820d-b188e54a6bbd","order_by":3,"name":"Christos Grecos","email":"","orcid":"","institution":"University of Wisconsin–Parkside","correspondingAuthor":false,"prefix":"","firstName":"Christos","middleName":"","lastName":"Grecos","suffix":""}],"badges":[],"createdAt":"2026-03-28 14:39:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9253567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9253567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107424156,"identity":"211c27bb-cc8d-4d07-97f0-75494bb374d1","added_by":"auto","created_at":"2026-04-21 10:57:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2214976,"visible":true,"origin":"","legend":"","description":"","filename":"paperlatex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9253567/v1_covered_87180ea9-6fa9-473f-9472-f82e4c05b6c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design of Marine Oil Spill Emergency Resource Scheduling Framework Based on Improved A*-PSO-SA-BPPO","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"operations-research-forum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Operations Research Forum](https://link.springer.com/journal/43069)","snPcode":"43069","submissionUrl":"https://submission.nature.com/new-submission/43069/3","title":"Operations Research Forum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Marine oil spill, Emergency resource scheduling, PSO, PPO, Path planning, Bayesian estimation","lastPublishedDoi":"10.21203/rs.3.rs-9253567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9253567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nMarine oil spills cause severe damage to ocean ecosystems and coastal economies. Timely scheduling of oil spill emergency resources is crucial for effective emergency response. Addressing the challenge of efficient resource scheduling under dynamically evolving oil spill conditions in marine environments, this paper proposes a hierarchical hybrid intelligent architecture. Firstly, it integrates an improved A*-PSO path planning module and an attention-based Bayesian Proximal Policy Optimization (SA-BPPO) reinforcement learning module to construct a 'path navigation-decision optimization' model for oil spill emergency resource scheduling. The improved A*-PSO path planning module generates initial paths and performs smoothing optimization, enabling rapid planning of efficient paths from start to end points, enhancing their feasibility and safety. Secondly, the self-attention Bayesian PPO (SA-BPPO) reinforcement learning module focuses on optimizing ship cleaning strategies. By incorporating an attention mechanism, the model’s ability to focus on key information is enhanced. The Bayesian method estimates the uncertainty of strategy outputs, allowing ships to dynamically adjust cleaning strategies according to environmental changes. Experimental results demonstrate that the proposed architecture significantly outperforms algorithms like Deep Q-Learning (DQN), Soft Actor-Critic (SAC), Ant Colony Optimization (ACO), and Dijkstra in key indicators such as cleaning time, efficiency, and path optimization. Compared to DQN, the improved A*-PSO-SA-BPPO algorithm improves average cleaning efficiency by 36\\%. Compared to SAC, it improves average cleaning efficiency by 9.17\\%. Compared to ACO and Dijkstra algorithms, it improves average cleaning efficiency by 36.78\\% and 75\\%, respectively. Ablation experiments further validate the effectiveness of each module that adding the improved A*-PSO module reduces cleaning time by 17.24\\% and increases cleaning efficiency by 39.45\\%.\n","manuscriptTitle":"Design of Marine Oil Spill Emergency Resource Scheduling Framework Based on Improved A*-PSO-SA-BPPO","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:55:13","doi":"10.21203/rs.3.rs-9253567/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"183650984893712524619935330926522921121","date":"2026-04-17T10:29:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T20:44:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-29T22:20:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-29T22:19:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Operations Research Forum","date":"2026-03-28T14:34:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"operations-research-forum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Operations Research Forum](https://link.springer.com/journal/43069)","snPcode":"43069","submissionUrl":"https://submission.nature.com/new-submission/43069/3","title":"Operations Research Forum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"32b8af86-ede6-4681-b288-5f41df132a75","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T10:55:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:55:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9253567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9253567","identity":"rs-9253567","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}