SC-DGLA: Constraint-Aware Pallet Demand Forecasting with Dynamic Graph and Learnable Lag Alignment

SC-DGLA: Constraint-Aware Pallet Demand Forecasting with Dynamic Graph and Learnable Lag Alignment | 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 SC-DGLA: Constraint-Aware Pallet Demand Forecasting with Dynamic Graph and Learnable Lag Alignment Ye Bin, Zheng Junhong, Zhu Linfei, Chen Yan, Lei Xiaofeng, Zhang Bo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8328371/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Accurate and actionable pallet demand forecasting is essential for multi-echelon warehouse supply chains, particularly when facing complex inter-node dependencies and time-varying transportation lags. Existing methods, despite progress in modeling supply chain dynamics, still struggle with temporal misalignment of multi-source signals and with enforcing operational constraints, limiting the direct usability of their forecasts. We propose SC-DGLA, a constraint-aware forecasting framework that integrates dynamic graph learning with conditional learnable lag alignment (LLA). It employs dynamic graph modules to capture evolving network structures and perform multi-task edge-level predictions, and incorporates conditional LLA to temporally align production, transfer, sales, and return signals. Constraint-aware training with projection-based decoding then ensures feasibility and yields decision-ready outputs. Experiments on real-world pallet data from the central warehouse of a large retail enterprise show that SC-DGLA maintains accuracy comparable to strong baselines while achieving a forecast feasibility rate of 92.8% and reducing shortage/overcapacity rates to 3.1% and 2.7%, respectively, offering more practical and operationally feasible forecasting support for warehouse planning and decision-making. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jan, 2026 Reviews received at journal 13 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers invited by journal 22 Dec, 2025 Editor assigned by journal 22 Dec, 2025 Editor invited by journal 17 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 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. 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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-8328371","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":564416390,"identity":"d4682fe2-b320-4a86-b34a-44b89b0e4c1b","order_by":0,"name":"Ye Bin","email":"","orcid":"","institution":"China Tobacco Zhejiang Industrial Co","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Bin","suffix":""},{"id":564416391,"identity":"ca29bf91-68cf-4c3f-9b2e-846490139efe","order_by":1,"name":"Zheng 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