Modeling and solving method for multi task engineering project scheduling considering nonlinear time-varying constraints

Modeling and solving method for multi task engineering project scheduling considering nonlinear time-varying constraints | 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 Modeling and solving method for multi task engineering project scheduling considering nonlinear time-varying constraints Zihan Wang, Zhihao Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8976850/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In response to the widespread problems of resource dynamic fluctuations, uncertain task duration, and time-varying costs in practical engineering projects, the author proposes a multi task engineering project scheduling model that considers nonlinear time-varying constraints, and designs a hybrid intelligent algorithm (IMHA) that integrates genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) for solution. This model covers multi-objective function construction, resource/time/risk constraint modeling, and fuzzy uncertainty handling. Through 35 sets of test cases with different scales and industry backgrounds, the experimental results show that IMHA is significantly better than GA, PSO, and SA in terms of project completion time, resource utilization, and algorithm stability, with an average reduction of about 11.5% in project duration and a 9.6% improvement in resource utilization. In addition, in the large-scale scheduling problem of 200 tasks, the running time is controlled within 15.6 minutes, demonstrating excellent scheduling efficiency and robustness. Nonlinear time-varying constraints Multi task scheduling Hybrid intelligent algorithm Resource optimization Fuzzy Uncertainty Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor invited by journal 12 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 26 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. We do this by developing innovative software and high quality services for the global research community. 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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-8976850","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608359356,"identity":"2b7e64f6-1d0b-4f59-969a-a8aa5a7dce2c","order_by":0,"name":"Zihan Wang","email":"","orcid":"","institution":"Dalian University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Wang","suffix":""},{"id":608359357,"identity":"caf8b87e-cb12-40d0-98a6-5e8d0f339fdf","order_by":1,"name":"Zhihao Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3PIQvCQBjG8XcMLp2svmNDv8Is1n2VHYNZFIwXDIJyC2oX/BJG6xCWzn7BMIvJYrOIvmJUNm2G+8Nx5X4cD4DN9ofFK0fRdecAblElctxMnBdBIiyNKl1+Q4A9CR3e848zt5m4wVRVV4mht1aZFBMGXj5PagkLi7y70MjxsCuN2IaAer+pJRyFwpaiLSbLjNAMIhzWEyTi34h0zKA3EsptJhGR4PlLRAS+JyFt6ZosxUSXvHFLvOqf/LPEuG3S4nKV47aXL+vJW/y35zabzWb72AM+6UXwHfECPwAAAABJRU5ErkJggg==","orcid":"","institution":"Dalian University","correspondingAuthor":true,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-02-26 10:54:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8976850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8976850/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035655,"identity":"70f0c3db-d2fb-4048-8a14-96778e341a21","added_by":"auto","created_at":"2026-03-20 07:26:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812409,"visible":true,"origin":"","legend":"","description":"","filename":"paper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8976850/v1_covered_4a0a0ed3-0f80-4df6-872f-98241bd06d0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling and solving method for multi task engineering project scheduling considering nonlinear time-varying constraints","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":"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":"Nonlinear time-varying constraints, Multi task scheduling, Hybrid intelligent algorithm, Resource optimization, Fuzzy Uncertainty","lastPublishedDoi":"10.21203/rs.3.rs-8976850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8976850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn response to the widespread problems of resource dynamic fluctuations, uncertain task duration, and time-varying costs in practical engineering projects, the author proposes a multi task engineering project scheduling model that considers nonlinear time-varying constraints, and designs a hybrid intelligent algorithm (IMHA) that integrates genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) for solution. 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