基于多任务深度学习的不确定性感知交付延迟持续时间预测
Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
AI总结：
本文提出一种多任务深度学习模型，通过端到端训练提升延迟运输检测和概率预测能力，有效应对高不平衡和异质性物流数据挑战。
摘要：
准确的交付延迟预测对于维持现代供应链的运营效率和客户满意度至关重要。然而，随着物流网络的复杂性增加，涵盖多模式运输、跨国家路由和显著区域差异，使预测任务本身变得极具挑战性。本文介绍了一种多任务深度学习模型，用于在存在显著不平衡数据的情况下预测交付延迟持续时间，其中延迟运输是罕见的但运营上具有重要影响。该模型嵌入高维运输特征，使用专用嵌入层处理表格数据，然后采用分类后再回归的策略来预测准时和延迟运输的交付延迟持续时间。与顺序流程不同，这种方法实现了端到端训练，提高了延迟情况的检测能力，并支持概率预测以支持不确定性感知的决策制定。所提出的方法在来自工业合作伙伴的大型真实世界数据集上进行了评估，该数据集包含超过1000万条历史运输记录，涵盖四个主要来源地点，具有不同的区域特征。所提出的方法与传统机器学习方法进行了比较。实验结果表明，所提出的方法在延迟运输预测中实现了平均绝对误差为0.67-0.91天，优于单步树形回归基线方法41-64%，以及两步分类后再回归树形模型方法15-35%。这些收益证明了所提出的方法在高度不平衡和异质条件下进行运营交付延迟预测的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
