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[2602.20271] Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
Summary
This paper presents a multi-task deep learning model for predicting delivery delay durations in logistics, addressing challenges posed by imbalanced data and complex networks.
Why It Matters
Accurate delivery delay predictions are crucial for enhancing operational efficiency and customer satisfaction in supply chains. This research introduces a novel approach that leverages deep learning to improve predictions in the face of significant data challenges, making it relevant for industries reliant on logistics.
Key Takeaways
- The proposed model uses a classification-then-regression strategy for better delay predictions.
- It outperforms traditional methods by 41-64% in accuracy for delayed shipments.
- The model is designed to handle imbalanced datasets effectively, crucial for real-world applications.
- End-to-end training enhances the detection of delayed cases and supports probabilistic forecasting.
- Evaluated on a dataset of over 10 million records, demonstrating practical applicability.
Computer Science > Machine Learning arXiv:2602.20271 (cs) [Submitted on 23 Feb 2026] Title:Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning Authors:Stefan Faulkner, Reza Zandehshahvar, Vahid Eghbal Akhlaghi, Sebastien Ouellet, Carsten Jordan, Pascal Van Hentenryck View a PDF of the paper titled Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning, by Stefan Faulkner and 5 other authors View PDF HTML (experimental) Abstract: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 o...
