用多任务学习预测物流延误时长，提升罕见延迟事件的识别与不确定性评估。
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
- 分步建模：先分类后回归，端到端训练提升延迟预测精度。
- 延迟订单预测误差仅0.67-0.91天，比基线模型提升41%-64%。
- 适合需高可靠性预测的供应链管理、物流调度场景。
准确预测物流延误对维持现代供应链运营效率与客户满意度至关重要。然而，多式联运、跨国路由及显著区域差异带来的复杂性使该任务极具挑战。本文提出一种针对数据严重不平衡（延迟货件稀少但影响重大）的多任务深度学习模型，通过专用嵌入层处理高维运输单特征，并采用分类-回归策略，分别预测准时与延迟货物的交付时长。该方法非顺序流程，支持端到端训练，增强延迟事件检测能力，并实现概率化预测以支持不确定性决策。在来自工业合作伙伴的大规模真实数据集上验证，涵盖超过1000万条历史发货记录，覆盖四个具有不同区域特征的始发地。实验表明，该模型对延迟货物的预测平均绝对误差为0.67–0.91天，优于单一阶段树基回归基线模型41%–64%，也优于两阶段分类-回归树模型15%–35%。结果证明该模型在高度不平衡且异构条件下具备优异的运营级延迟预测能力。
原文摘要 · Abstract (English)
Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this prediction task inherently challenging. This paper introduces a multi-task deep learning model for delivery delay duration prediction in the presence of significant imbalanced data, where delayed shipments are rare but operationally consequential. The model embeds high-dimensional shipment features with dedicated embedding layers for tabular data, and then uses a classification-then-regression strategy to predict the delivery delay duration for on-time and delayed shipments. Unlike sequential pipelines, this approach enables end-to-end training, improves the detection of delayed cases, and supports probabilistic forecasting for uncertainty-aware decision making. The proposed approach is evaluated on a large-scale real-world dataset from an industrial partner, comprising more than 10 million historical shipment records across four major source locations with distinct regional characteristics. The proposed model is compared with traditional machine learning methods. Experimental results show that the proposed method achieves a mean absolute error of 0.67-0.91 days for delayed-shipment predictions, outperforming single-step tree-based regression baselines by 41-64% and two-step classify-then-regress tree-based models by 15-35%. These gains demonstrate the effectiveness of the proposed model in operational delivery delay forecasting under highly imbalanced and heterogeneous conditions.
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