Import container dwell time (ICDT) prediction is a key task for improving productivity in container terminals, as accurate predictions enable the reduction of container re-handling operations by yard cranes. Achieving this objective requires accurately predicting the dwell time of individual containers. However, the primary determinants of dwell time-owner information and cargo information-are recorded as unstructured text, which limits their effective use in machine learning models. This study addresses this limitation by proposing a collaborative framework that integrates generative artificial intelligence (Gen AI) with machine learning. The proposed framework employs Gen AI to standardize unstructured information into standard international codes, with dynamic re-prediction triggered by electronic data interchange state updates, enabling the machine learning model to predict ICDT accurately. Extensive experiments conducted on real container terminal data demonstrate that the proposed methodology achieves a 13.88% improvement in mean absolute error compared to conventional models that do not utilize standardized information. Furthermore, applying the improved predictions to container stacking strategies achieves up to 14.68% reduction in the number of relocations, thereby empirically validating the potential of Gen AI to enhance productivity in container terminal operations. Overall, this study provides both technical and methodological insights into the adoption of Gen AI in port logistics and its effectiveness.

翻译：进口集装箱滞留时间预测是提升集装箱码头生产效率的关键任务，准确的预测能够通过减少场桥的翻箱操作来实现这一目标。达成此目标需要精确预测单个集装箱的滞留时间。然而，决定滞留时间的主要因素——货主信息和货物信息——均以非结构化文本形式记录，这限制了其在机器学习模型中的有效利用。本研究通过提出一个集成生成式人工智能与机器学习的协同框架来解决这一局限。该框架利用生成式人工智能将非结构化信息标准化为国际标准代码，并通过电子数据交换状态更新触发动态重预测，从而使机器学习模型能够准确预测进口集装箱滞留时间。在真实集装箱码头数据上进行的大量实验表明，与未使用标准化信息的传统模型相比，所提方法在平均绝对误差上实现了13.88%的提升。此外，将改进后的预测应用于集装箱堆存策略，可实现高达14.68%的翻箱次数减少，从而从经验上验证了生成式人工智能在提升集装箱码头运营效率方面的潜力。总体而言，本研究为生成式人工智能在港口物流中的采用及其有效性提供了技术与方法论的见解。