A Genetic Algorithm Approach for Controlling Mobile Rack Automated Storage and Retrieval System

A Genetic Algorithm Approach for Controlling Mobile Rack Automated Storage and Retrieval System | 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 Genetic Algorithm Approach for Controlling Mobile Rack Automated Storage and Retrieval System Bessenouci Hakim nadhir, Sihem KOULOUGHLI, Latefa GHOMRI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8060116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract In this study, we address the sequencing problem of storage and retrieval operations in a Mobile Rack Automated Storage and Retrieval System (MR-AS/RS). This problem is particularly relevant to AS/RS configurations equipped with a multi-shuttle crane, where the coordination and optimization of operations directly affect system performance. The goal is to create and test effective sequencing strategies that cut down on operation time while increasing the overall system throughput. To tackle this challenge, this paper proposes the use of a specially tailored Genetic Algorithm (GA) to optimize the sequencing of storage and retrieval tasks in MR-AS/RSs. The developed GA aims to minimize the total travel time of both single- and multi-shuttle Storage/Retrieval (S/R) machines. Its performance is compared with classical heuristics and analytical models from the literature, which were originally designed to optimize similar AS/RSs. These analytical models also serve as a theoretical basis for assessing the effectiveness of the proposed control approach. A comprehensive simulation study was conducted under various system configurations, considering changes in the number of aisles, shape factors as well as item diversity. Additionally, a sensitivity analysis of the GA parameters was performed to identify the optimal settings and validate the robustness and efficiency of the proposed GA in solving the sequencing problem for MR-AS/RSs. Mobile Rack-Automated Storage Retrieval System Mobile Rack AS/RS Metaheuristics Genetic Algorithm Simulated Annealing Taboo Search Nearest Neighbor Heuristics AS/RS Control Full Text Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 08 Jan, 2026 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 13 Nov, 2025 Editor assigned by journal 13 Nov, 2025 First submitted to journal 11 Nov, 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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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-8060116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544578278,"identity":"75856eea-501a-48ad-aa6f-46de6fa8235a","order_by":0,"name":"Bessenouci Hakim 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This problem is particularly relevant to AS/RS configurations equipped with a multi-shuttle crane, where the coordination and optimization of operations directly affect system performance. The goal is to create and test effective sequencing strategies that cut down on operation time while increasing the overall system throughput. To tackle this challenge, this paper proposes the use of a specially tailored Genetic Algorithm (GA) to optimize the sequencing of storage and retrieval tasks in MR-AS/RSs. The developed GA aims to minimize the total travel time of both single- and multi-shuttle Storage/Retrieval (S/R) machines. Its performance is compared with classical heuristics and analytical models from the literature, which were originally designed to optimize similar AS/RSs. These analytical models also serve as a theoretical basis for assessing the effectiveness of the proposed control approach. A comprehensive simulation study was conducted under various system configurations, considering changes in the number of aisles, shape factors as well as item diversity. 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