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<p>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.</p>

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SC-DGLA: constraint-aware pallet demand forecasting with dynamic graph and learnable lag alignment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

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  • Bin Ye,
  • Junhong Zheng,
  • Linfei Zhu,
  • Yan Chen,
  • Xiaofeng Lei,
  • Bo Zhang,
  • Yijun Wang

摘要

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.