Resource leveling optimization by hybrid metaheuristics

Resource leveling optimization by hybrid metaheuristics | 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 Resource leveling optimization by hybrid metaheuristics Marinos Aristotelous, Andreas Nearchou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2635672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms are investigated as low-level hybrids in the context of three famous population-based heuristics namely, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as parallel and serial HS respectively which combine DE, GA and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex data sets measures the efficiency of the new hybrids. Statistical analysis employed rank the hybrids according to their solution efficiency. Moreover, comparisons between the developed best hybrid and commercial project management software show a substantial higher performance for the former over real-world construction projects. metaheuristics combinatorial optimization evolutionary algorithms swarm intelligence project resources scheduling smoothing levelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-2635672","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":179579232,"identity":"19c696c2-bc8d-416c-9d70-11f80fd53efd","order_by":0,"name":"Marinos Aristotelous","email":"","orcid":"","institution":"University of Patras","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marinos","middleName":"","lastName":"Aristotelous","suffix":""},{"id":179579233,"identity":"a92357af-9dac-46c3-80be-54a65bd2fdb3","order_by":1,"name":"Andreas Nearchou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACxgYGBn7StUgCMcMBkrQYHCBWizl787MPP3fYJG6+kXvw88c9dvYM/Gsf4NVi2XPMeGbvmbTEbTfykiUOPEtObJB4boBXi8GNBGNmxrbDQC05BhIHDjAnMEgcw+8wg/vPP4O1bJ6RY/zjwIF6e8JabvBAbNkgkWMGtOUwYwN/GwEtZ3KKGXvb0oxnnHmXZnHmwPHENgk2AlqOH9/M8LPNRra/PffwjYoD1fb8/AQchgR4IBSbRAKpWhj4DxCtZRSMglEwCkYGAABiJkwdza4w/AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Patras","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Nearchou","suffix":""}],"badges":[],"createdAt":"2023-02-27 20:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2635672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2635672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42420872,"identity":"cbd70176-7d99-41cd-b157-efceb56d3a76","added_by":"auto","created_at":"2023-08-31 12:07:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505628,"visible":true,"origin":"","legend":"","description":"","filename":"aticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2635672/v1_covered_cd94e0e8-bb60-49f2-94e9-c108755d2e0a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resource leveling optimization by hybrid metaheuristics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"metaheuristics, combinatorial optimization, evolutionary algorithms, swarm intelligence, project, resources, scheduling, smoothing, levelling","lastPublishedDoi":"10.21203/rs.3.rs-2635672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2635672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConsideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms are investigated as low-level hybrids in the context of three famous population-based heuristics namely, \u003cem\u003edifferential evolution\u003c/em\u003e (DE), \u003cem\u003egenetic algorithm\u003c/em\u003e (GA) and \u003cem\u003eparticle swarm optimization\u003c/em\u003e (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as \u003cem\u003eparallel\u003c/em\u003e and \u003cem\u003eserial\u003c/em\u003e HS respectively which combine DE, GA and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex data sets measures the efficiency of the new hybrids. 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