Real-World Evaluation of Hybrid Green AI for Sustainable and Efficient Smart Supply Chain Distribution

Real-World Evaluation of Hybrid Green AI for Sustainable and Efficient Smart Supply Chain Distribution | 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 Real-World Evaluation of Hybrid Green AI for Sustainable and Efficient Smart Supply Chain Distribution Mohamed Ahmed Hassouna, Amal Elsayed Aboutabl, Naglaa Mohamed Diaa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7768705/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Optimizing physical distribution in smart supply chains requires a balance between efficiency and environmental sustainability. This paper proposes a hybrid green AI framework, integrated with real-world geospatial data, to achieve multi-objective logistics optimization. The framework combines metaheuristics and multi-objective optimization (MOO) algorithms to identify delivery routes that minimize costs, time, fuel consumption, and CO2 emissions while maintaining operational efficiency. The system uses Google Maps directions to obtain accurate distance and travel time matrices, ensuring that routing decisions reflect actual road networks, traffic, and terrain constraints. The optimization layer uses OR tools to prototype multi-objective optimized ant colony (MOIAC) and multi-objective optimized particle swarm (MOIPS) algorithms, enabling scalable and efficient computing. Dynamic demand patterns are simulated using Zipf stochastic distribution models, allowing for adaptation to irregular customer demand across urban and rural nodes. Experiments conducted in 19 Egyptian cities showed that the MOIAC algorithm reduces total distance, operating costs, and carbon emissions by 26.7% compared to baseline methods. It also achieves the lowest average response time (120 ms) under highly skewed demand (Zipf coefficient α = 0.9), outperforming other algorithms. The proposed framework integrates green AI, geospatial intelligence, and multi-objective optimization to provide a practical and sustainable solution for smart supply chain distribution, while balancing operational performance and environmental responsibility. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Green AI Smart Supply Chain Distribution Multi-Objective Optimization Hybrid Metaheuristics Sustainable Logistics CO₂ Reduction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 28 Oct, 2025 Editor assigned by journal 28 Oct, 2025 Editor invited by journal 10 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 Oct, 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. 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-7768705","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538610131,"identity":"37a4df0c-106c-49cc-a2bc-3b3a887d93d1","order_by":0,"name":"Mohamed Ahmed 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This paper proposes a hybrid green AI framework, integrated with real-world geospatial data, to achieve multi-objective logistics optimization. The framework combines metaheuristics and multi-objective optimization (MOO) algorithms to identify delivery routes that minimize costs, time, fuel consumption, and CO2 emissions while maintaining operational efficiency. The system uses Google Maps directions to obtain accurate distance and travel time matrices, ensuring that routing decisions reflect actual road networks, traffic, and terrain constraints. The optimization layer uses OR tools to prototype multi-objective optimized ant colony (MOIAC) and multi-objective optimized particle swarm (MOIPS) algorithms, enabling scalable and efficient computing. Dynamic demand patterns are simulated using Zipf stochastic distribution models, allowing for adaptation to irregular customer demand across urban and rural nodes. 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