This study develops a deep learning-based approach to automate inbound load plan adjustments for a large transportation and logistics company. It addresses a critical challenge for the efficient and resilient planning of E-commerce operations in presence of increasing uncertainties. The paper introduces an innovative data-driven approach to inbound load planning. Leveraging extensive historical data, the paper presents a two-stage decision-making process using deep learning and conformal prediction to provide scalable, accurate, and confidence-aware solutions. The first stage of the prediction is dedicated to tactical load-planning, while the second stage is dedicated to the operational planning, incorporating the latest available data to refine the decisions at the finest granularity. Extensive experiments compare traditional machine learning models and deep learning methods. They highlight the importance and effectiveness of the embedding layers for enhancing the performance of deep learning models. Furthermore, the results emphasize the efficacy of conformal prediction to provide confidence-aware prediction sets. The findings suggest that data-driven methods can substantially improve decision making in inbound load planning, offering planners a comprehensive, trustworthy, and real-time framework to make decisions. The initial deployment in the industry setting indicates a high accuracy of the proposed framework.

翻译：本研究为一家大型运输与物流公司开发了一种基于深度学习的自动化入站装载计划调整方法。该方法针对电子商务运营在不确定性日益增加的背景下实现高效、弹性规划的关键挑战，提出了一种创新的数据驱动式入站装载规划方案。通过利用海量历史数据，本文提出了一个结合深度学习与保形预测的两阶段决策流程，以提供可扩展、精准且具备置信度感知的解决方案。预测的第一阶段专注于战术性装载规划，第二阶段则致力于操作性规划，通过整合最新可用数据实现最精细粒度的决策优化。大量实验对比了传统机器学习模型与深度学习方法，结果表明嵌入层对于提升深度学习模型性能具有重要影响与显著效果。此外，实验结果凸显了保形预测在生成置信度感知预测集方面的有效性。研究结论表明，数据驱动方法能够显著改善入站装载规划的决策质量，为规划者提供全面、可靠且实时的决策框架。该框架在工业场景中的初步部署验证了其高精度特性。