Research on the Improvement of Bacterial Foraging Algorithm for Flexible Job shop Scheduling Problem

Research on the Improvement of Bacterial Foraging Algorithm for Flexible Job 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 Research on the Improvement of Bacterial Foraging Algorithm for Flexible Job shop Scheduling Problem Xiaoyan Wang, Shuaiwen Wang, Taoliang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5377579/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 Machine workload is an important factor to be considered in flexible job shop scheduling, and the existing research has achieved certain results in optimizing the total workload and the maximum machine workload, but the workload balancing among machines is insufficiently considered, the workload imbalance will lead to problems such as underutilization of production potential and machine overload failure. Therefore, this paper first proposes a flexible job shop scheduling problem that considers workload balancing, and an optimization model was established with the goal of minimizing the maximum completion time(Makespan) and minimizing the workload balancing factor, aiming to fully unleash the production potential while avoiding machine overload and further improve the production efficiency. In order to effectively solve the proposed problem, a series of improvements were made to optimize the defects of the bacterial foraging optimization algorithm(BFOA), such as easy to precocious convergence and the “escape” of elite individuals, an improved bacterial foraging optimization algorithm(IBFOA) was proposed. The proposed algorithm designs and introduces Logistic-Circle chaos mapping to enhance the quality of the initial population;An adaptive dynamic step size is designed to ensure the optimization efficiency and accuracy; Improved reproduction operation to avoid precocious convergence; An adaptive migration probability is designed to avoid the "escape" of elite individuals. The results of the case testing show the effectiveness of the proposed model and the superiority of the improved algorithm. flexible job shop scheduling Makespan workload balancing Chaos mapping Adaptive improved bacterial foraging optimization algorithm 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. 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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-5377579","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383863342,"identity":"8198034d-ef1e-48ea-99d8-7865f802f285","order_by":0,"name":"Xiaoyan Wang","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Wang","suffix":""},{"id":383863344,"identity":"e2e4c339-d5f2-439a-bc91-c4ad9353a61a","order_by":1,"name":"Shuaiwen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3RsQqCUBSA4SOC04XWI4E+QXBBCF30VZRewpYyCKdeIPAhgkAaj1y405VWG12aHIrmoLUmb1vQ/fePcw4HwGT6wSYgiDBHLyFBesTdyIxCFQbQyFSPcKECWm7zrBCKa24midOlQGtTqns3QOzNihFh7Sht9ie0bdYeowoWwZxGiI1EwlXoONjWUwaU1WPE8ftCPEtkzB+ueoSBAHJLRATl6BEECYQKOQcZRBXXuCWh8+OG+Wp9ANF3Qx57o+RzJNN9zRv5VphMJtNf9ALXPEwJaZg82gAAAABJRU5ErkJggg==","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":true,"prefix":"","firstName":"Shuaiwen","middleName":"","lastName":"Wang","suffix":""},{"id":383863345,"identity":"b8b3e0b8-bcf5-41f6-bd6c-80adb9de89e9","order_by":2,"name":"Taoliang Wang","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Taoliang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-11-02 09:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5377579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5377579/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76298022,"identity":"7b0fe237-9fe2-46cf-8d9b-c6e2866ced13","added_by":"auto","created_at":"2025-02-14 13:32:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1003954,"visible":true,"origin":"","legend":"","description":"","filename":"Article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5377579/v1_covered_3a0ed8a9-eaee-4b86-b593-7804c0f93aca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Improvement of Bacterial Foraging Algorithm for Flexible Job shop Scheduling Problem","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":"flexible job shop scheduling, Makespan, workload balancing, ,Chaos mapping,Adaptive, improved bacterial foraging optimization algorithm","lastPublishedDoi":"10.21203/rs.3.rs-5377579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5377579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine workload is an important factor to be considered in flexible job shop scheduling, and the existing research has achieved certain results in optimizing the total workload and the maximum machine workload, but the workload balancing among machines is insufficiently considered, the workload imbalance will lead to problems such as underutilization of production potential and machine overload failure. Therefore, this paper first proposes a flexible job shop scheduling problem that considers workload balancing, and an optimization model was established with the goal of minimizing the maximum completion time(Makespan) and minimizing the workload balancing factor, aiming to fully unleash the production potential while avoiding machine overload and further improve the production efficiency. In order to effectively solve the proposed problem, a series of improvements were made to optimize the defects of the bacterial foraging optimization algorithm(BFOA), such as easy to precocious convergence and the \u0026ldquo;escape\u0026rdquo; of elite individuals, an improved bacterial foraging optimization algorithm(IBFOA) was proposed. The proposed algorithm designs and introduces Logistic-Circle chaos mapping to enhance the quality of the initial population;An adaptive dynamic step size is designed to ensure the optimization efficiency and accuracy; Improved reproduction operation to avoid precocious convergence; An adaptive migration probability is designed to avoid the \"escape\" of elite individuals. 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