A knowledge-driven memetic algorithm for energy-aware flexible job shop scheduling with limited AGV transportation

A knowledge-driven memetic algorithm for energy-aware flexible job shop scheduling with limited AGV transportation | 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 knowledge-driven memetic algorithm for energy-aware flexible job shop scheduling with limited AGV transportation Yajie Huo, Fayong Zhang, Wenyin Gong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7242105/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Memetic Computing → Version 1 posted 11 You are reading this latest preprint version Abstract Flexible Job Shop Scheduling Problems (FJSP) traditionally assumeinfinite transportation resources or simplified transportation constraints.With the rise of intelligent manufacturing,Automated Guided Vehicles (AGVs) have emerged as essential transport resources due to their high flexibility and autonomy.The limited availability of AGVs can significantly impact overall production efficiency.Meanwhile, growing concerns about energy consumption and environmental sustainabilityunderscore the necessity of incorporating energy-related objectives into scheduling decisions.In this context, this paper addresses the Energy-aware FJSP with limited AGVs (EFJSP-AGV).A multi-objective mixed-integer programming (MMIP) model is developedto simultaneously minimize makespan and total energy consumption (TEC).To efficiently tackle this challenging problem,a knowledge-driven memetic algorithm (KDMA) is proposed.Specifically, an integrated initialization approach is devisedto efficiently generate promising initial solutions.Furthermore, a knowledge-driven variable neighborhood search (VNS) tailored tothe characteristics of the problem is developedto enhance the exploitation within the solution space.Additionally, effective energy-aware strategies for reducing energy consumption are incorporatedto achieve lower total energy usage.Experimental results indicate that the proposed KDMA outperforms comparison algorithms,validating its effectiveness in solving the EFJSP-AGV. Flexible job shop scheduling Automated guided vehicle Energy-aware scheduling Multi-objective optimization Memetic algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Memetic Computing → Version 1 posted Editorial decision: Revision requested 30 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 05 Aug, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 29 Jul, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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