Hybrid Grey Wolf Optimizer with Discrete Prism Dispersion Strategy for Solving Flexible Job-Shop Scheduling Problem

Hybrid Grey Wolf Optimizer with Discrete Prism Dispersion Strategy for Solving 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 Article Hybrid Grey Wolf Optimizer with Discrete Prism Dispersion Strategy for Solving Flexible Job-Shop Scheduling Problem Ying Duan, Luyi Shi, Mingyang Li, Kangmin Hua, Ting Liu, Lijun He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7493527/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Flexible job-shop scheduling problem (FJSP) is a quintessential NP-hard problem in the field of production scheduling. With the development of intelligent manufacturing industry, minimizing the sum of total completion times in workshops has become a crucial research focus. Swarm intelligence algorithms provide common methods to solve FJSP. However, they still suffer from issues such as premature convergence and a tendency of trapping in local optimum. In addition, as iterations increase, the basic parameters of the algorithm still need to be flexibly adjusted.To address these challenges, we propose a hybrid grey wolf optimization algorithm incorporating discrete prism dispersion strategy (HGWO-DPDS). First, in the position update stage, a critical path-guided mechanism is introduced in the operation sequencing stage to identify and perturb bottleneck operations, while in the machine selection stage, the convergence ability toward optimal solution is enhanced with machine optimal guidance. Secondly, the dispersion strategy is integrated to diversify the search directionsexpand through multiple reference centers. Finally, an adaptive mutation operator is applied to maintain population diversity and prevent stagnation.We conduct a comprehensive evaluation of the proposed model through benchmark experimentson on three widely used datasets, namely the MK, Kacem, and Lawrence instances. HGWO-DPDS is compared with several existing algorithms. The experimental results demonstrate that the proposed framework achieves optimal or near-optimal makespan values on most instances, while maintaining reliable performance in solving the FJSP. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Editor invited by journal 03 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 02 Sep, 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. 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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-7493527","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":527351360,"identity":"b3da7113-f4e7-455f-95a2-102e108a78fc","order_by":0,"name":"Ying Duan","email":"","orcid":"","institution":"Zhengzhou University of Aeronautics","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Duan","suffix":""},{"id":527351361,"identity":"772b691b-4cfe-4609-9869-9f28f7aa5efb","order_by":1,"name":"Luyi Shi","email":"","orcid":"","institution":"Zhengzhou University of 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