Recent advancements in generative artificial intelligence (AI) have demonstrated its substantial potential in various fields. However, its application in port logistics remains underexplored. Ports are complex operational environments where diverse types of contextual information coexist, making them a promising domain for the implementation of generative AI and highlighting the urgency of related research. In this study, we applied a large language model (LLM)-a leading generative AI technique-to forecast container throughput, which is a critical challenge in port logistics. To this end, we adopted a state-of-the-art LLM approach and proposed a novel prompt structure designed to incorporate the contextual characteristics of port operations. Extensive experiments confirm the superiority of our method, showing that the proposed approach outperforms competitive benchmark models. Furthermore, additional experiments revealed that LLMs can effectively learn and utilize multiple layers of contextual information for inference in port logistics. Based on these findings, we explore the key constraints affecting LLM adoption in this domain and outline future research directions aimed at addressing them. Accordingly, we offer both technical and practical insights to support the effective deployment of generative AI in port logistics.

翻译：生成式人工智能的最新进展已证明其在多个领域具有巨大潜力。然而，其在港口物流中的应用仍待深入探索。港口是复杂的运营环境，多种类型的情境信息共存，这使其成为实施生成式人工智能的有前景领域，并凸显了相关研究的紧迫性。在本研究中，我们应用了一种领先的生成式人工智能技术——大型语言模型（LLM）来预测集装箱吞吐量，这是港口物流中的一项关键挑战。为此，我们采用了一种先进的LLM方法，并提出了一种新颖的提示结构，旨在融入港口运营的情境特征。大量实验证实了我们方法的优越性，表明所提出的方法优于竞争性基准模型。此外，额外实验表明，LLMs能够有效学习和利用多层情境信息进行港口物流推理。基于这些发现，我们探讨了影响LLM在该领域应用的关键限制因素，并概述了旨在解决这些问题的未来研究方向。据此，我们提供了技术和实践见解，以支持生成式人工智能在港口物流中的有效部署。