Study on the Optimization of Natural Gas Station Inspection Paths Considering Station Priority

Study on the Optimization of Natural Gas Station Inspection Paths Considering Station Priority | 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 Study on the Optimization of Natural Gas Station Inspection Paths Considering Station Priority Xinyan Peng, Jingyi Li, Yuan Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7356923/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract As the demand for natural gas supply chain security increases, the optimization of inspection efficiency in natural gas stations is increasingly critical to ensure the safe operation of equipment. This study identifies key issues, including the disregard for equipment priority differences and inadequate coordination of multiple objectives in traditional inspection path planning. By integrating digitalization and artificial intelligence technologies, and considering both inspection costs and priority satisfaction, a priority satisfaction function is constructed, and a multi-objective optimization model is proposed to minimize inspection costs and maximize priority satisfaction. A hybrid genetic simulated annealing algorithm (GA-SA) is designed to solve this model. A case study was conducted to validate the model and algorithm’s effectiveness in reducing inspection costs and enhancing priority satisfaction. The study demonstrated that, compared to experience-based manual planning methods, the proposed model reduced inspection costs by approximately 20.70% and increased priority satisfaction by about 5.33%. Based on the research findings, a dynamic inspection path management strategy based on artificial intelligence is proposed, which provides intelligent decision support that balances both economic and safety considerations for the natural gas industry. This strategy also offers theoretical insights for multi-objective path optimization problems in complex scenarios. Physical sciences/Engineering Physical sciences/Mathematics and computing Natural gas station inspection Path optimization Station priority Artificial intelligence Digital transformation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviews received at journal 06 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 03 Sep, 2025 Editor assigned by journal 26 Aug, 2025 Editor invited by journal 22 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 18 Aug, 2025 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. 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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-7356923","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511910041,"identity":"e285054e-949f-4ff7-b076-9f6413cb9698","order_by":0,"name":"Xinyan Peng","email":"","orcid":"","institution":"Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Xinyan","middleName":"","lastName":"Peng","suffix":""},{"id":511910043,"identity":"2ae72b9c-e803-4296-b9d9-d53f177dd865","order_by":1,"name":"Jingyi Li","email":"","orcid":"","institution":"Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Li","suffix":""},{"id":511910044,"identity":"60cef044-26fd-45fa-ba71-1b2782ec4257","order_by":2,"name":"Yuan Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACNgbGBoYEAwkeNvbmAwc+/CBWy4cKCzl+nmOJB2f2EGkT44wzFcaSM3yMD3OwEaGcT/pw82feNonEDTd4Phxm4GGQ5xc7QMBhfIlt0mAtt3s3HC6wYDCcOTuBgBYexjZmsJY7ZzccnsEDDIrbhLXAHJbz4DAPG3FaGiRnnJEAej+HgWgtbRIfKiRAgWwADGQJwn6R72F//CHBoA4UlY8/fPhhI88vTUALOpAgTfkoGAWjYBSMAuwAAG6BRJ/hWi5sAAAAAElFTkSuQmCC","orcid":"","institution":"Southwest Petroleum University","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-08-12 14:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7356923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7356923/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-47229-8","type":"published","date":"2026-04-06T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":106808899,"identity":"bc3777ce-dc9b-435e-b565-8fe8d13e2aad","added_by":"auto","created_at":"2026-04-13 16:04:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":685456,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356923/v1_covered_5ad7cac2-82fc-4abd-9522-fb419ccc1fbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Optimization of Natural Gas Station Inspection Paths Considering Station Priority","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Natural gas station inspection, Path optimization, Station priority, Artificial intelligence, Digital transformation","lastPublishedDoi":"10.21203/rs.3.rs-7356923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7356923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the demand for natural gas supply chain security increases, the optimization of inspection efficiency in natural gas stations is increasingly critical to ensure the safe operation of equipment. 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