A Novel Hybrid Differential Evolutionary Algorithm for Solving Multi-objective Distributed Permutation Flow-shop Scheduling Problem

A Novel Hybrid Differential Evolutionary Algorithm for Solving Multi-objective Distributed Permutation Flow-shop Scheduling Problem | 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 A Novel Hybrid Differential Evolutionary Algorithm for Solving Multi-objective Distributed Permutation Flow-shop Scheduling Problem Xinzhe Du, Yanping Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5367783/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The Distributed Permutation Flow-shop Scheduling Problem (DPFSP) is a classic issue in distributed scheduling that involves job allocation and processing order within a factory, and it is known to be NP-hard. Numerous researchers have proposed various intelligent optimization algorithms to address the DPFSP; however, achieving solutions of sufficient quality to meet production practice requirements remains challenging. To tackle the multi-objective DPFSP, this paper proposes a novel hybrid differential evolutionary algorithm aimed at minimizing both the maximum completion time and delay time. In this algorithm, Bernoulli chaotic mapping is applied during the population initialization process to enhance the diversity of the initial population. Additionally, an adaptive mutation factor and crossover rate are designed to balance the global and local search capabilities of the algorithm. Furthermore, a novel selection strategy is constructed based on the NEH algorithm, specular reflection learning, and Pareto dominance relation to improve the quality of the solution set when solving instances of varying sizes. This strategy enhances the algorithm's optimization ability and helps it escape local optima. The effectiveness and superiority of the proposed algorithm are verified through 24 instances of different sizes. The results demonstrate that the proposed algorithm outperforms other improved algorithms in terms of convergence, and the uniformity and diversity of the solution set, making it an effective solution for the multi-objective distributed permutation flow-shop scheduling problem. Bernoulli chaotic mapping differential evolutionary distributed permutation flow-shop scheduling problem multi-objective optimization NEH algorithm specular reflection learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Dec, 2024 Reviews received at journal 30 Nov, 2024 Reviews received at journal 28 Nov, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers agreed at journal 22 Nov, 2024 Reviews received at journal 21 Nov, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers invited by journal 18 Nov, 2024 Editor assigned by journal 18 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 31 Oct, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5367783","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379499395,"identity":"7e2c6434-335e-4e10-a663-53c375f0fb32","order_by":0,"name":"Xinzhe Du","email":"","orcid":"","institution":"Qingdao University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinzhe","middleName":"","lastName":"Du","suffix":""},{"id":379499398,"identity":"c6328cfb-26c4-43ac-b82b-7505a1767ca8","order_by":1,"name":"Yanping Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIie3QMQrCMBSA4VcCnayuKVL1CBVHxbM0BOwNOjvFRZwjXiJHiAQ6NmugHerirJuLYMClg5i6OeSHl5ch3xIAn+8/C+2sOveeZGMH/UbUDyStd9f2znQitJZwKxSMTlsHaWQ+P7J6IQyFgFcKcCMdxGTlOGI1EQYBipiCFGcuQpglFRFaAXr2IzS0RBIhKaCgD4mbMox5RRdHQ9PzvsoH2DjI0P4YvhXr5KDPl/ZRLCcj7iAz897B1h7SzuD7e9uUd4jP5/P5PvYCrYhHwoaddKwAAAAASUVORK5CYII=","orcid":"","institution":"Qingdao University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-10-31 15:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5367783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5367783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69311240,"identity":"15bbe5a1-2737-4413-945e-f2cd2d3fad7e","added_by":"auto","created_at":"2024-11-19 05:08:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697695,"visible":true,"origin":"","legend":"","description":"","filename":"DuInternationalJournalofComputationalIntelligenceSystems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5367783/v1_covered_5f3b1d21-2304-4acc-8923-b8aa41d83e2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Hybrid Differential Evolutionary Algorithm for Solving Multi-objective Distributed Permutation Flow-shop Scheduling Problem","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":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bernoulli chaotic mapping, differential evolutionary, distributed permutation flow-shop scheduling problem, multi-objective optimization, NEH algorithm, specular reflection learning","lastPublishedDoi":"10.21203/rs.3.rs-5367783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5367783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Distributed Permutation Flow-shop Scheduling Problem (DPFSP) is a classic issue in distributed scheduling that involves job allocation and processing order within a factory, and it is known to be NP-hard. Numerous researchers have proposed various intelligent optimization algorithms to address the DPFSP; however, achieving solutions of sufficient quality to meet production practice requirements remains challenging. To tackle the multi-objective DPFSP, this paper proposes a novel hybrid differential evolutionary algorithm aimed at minimizing both the maximum completion time and delay time. In this algorithm, Bernoulli chaotic mapping is applied during the population initialization process to enhance the diversity of the initial population. Additionally, an adaptive mutation factor and crossover rate are designed to balance the global and local search capabilities of the algorithm. Furthermore, a novel selection strategy is constructed based on the NEH algorithm, specular reflection learning, and Pareto dominance relation to improve the quality of the solution set when solving instances of varying sizes. This strategy enhances the algorithm's optimization ability and helps it escape local optima. The effectiveness and superiority of the proposed algorithm are verified through 24 instances of different sizes. The results demonstrate that the proposed algorithm outperforms other improved algorithms in terms of convergence, and the uniformity and diversity of the solution set, making it an effective solution for the multi-objective distributed permutation flow-shop scheduling problem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"A Novel Hybrid Differential Evolutionary Algorithm for Solving Multi-objective Distributed Permutation Flow-shop Scheduling Problem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 05:00:08","doi":"10.21203/rs.3.rs-5367783/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-01T23:39:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T12:09:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T04:00:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62686337325164306722668610936262117330","date":"2024-11-25T01:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172184489242897215787622112676438539013","date":"2024-11-22T09:05:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-22T03:23:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266342021495016521289119969587140722513","date":"2024-11-22T00:48:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339381847356010443891204920933893173773","date":"2024-11-19T01:58:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299144619858340425931491718067782446518","date":"2024-11-18T13:33:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-18T13:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-18T08:58:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-05T13:09:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Computational Intelligence Systems","date":"2024-10-31T15:19:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b468afef-cca0-4c11-b928-3ef343652cbc","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T09:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 05:00:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5367783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5367783","identity":"rs-5367783","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}