用大模型自动调度港口车辆，省专家、少数据、快部署。
PortAgent: LLM-driven Vehicle Dispatching Agent for Port Terminals
- 构建虚拟专家团队，用大模型自动完成调度系统迁移
- 仅需少量案例即可适配不同码头，部署速度提升数倍
- 适合港口自动化团队快速落地智能调度系统
车辆调度系统（VDS）对自动化集装箱码头（ACT）的运营效率至关重要。然而，其商业化推广受限于跨码头的低可迁移性，主要源于三方面挑战：高度依赖码头运营专家、需要大量码头专属数据、部署过程耗时。本文提出PortAgent，一种由大语言模型驱动的车辆调度代理，可完全自动化实现VDS迁移流程。该系统具备三大特性：无需专业人员、数据需求低、部署迅速。通过虚拟专家团队（VET）消除专家依赖，该团队由知识检索、建模、编码和调试四名虚拟专家组成，借助少量示例学习方式掌握码头调度领域知识。知识通过检索增强生成（RAG）机制获取，显著降低对特定数据的需求。四名专家协同构建自动化设计流程，引入受LLM Reflexion框架启发的自校正循环，避免人工干预。
原文摘要 · Abstract (English)
Vehicle Dispatching Systems (VDSs) are critical to the operational efficiency of Automated Container Terminals (ACTs). However, their widespread commercialization is hindered due to their low transferability across diverse terminals. This transferability challenge stems from three limitations: high reliance on port operational specialists, a high demand for terminal-specific data, and time-consuming manual deployment processes. Leveraging the emergence of Large Language Models (LLMs), this paper proposes PortAgent, an LLM-driven vehicle dispatching agent that fully automates the VDS transferring workflow. It bears three features: (1) no need for port operations specialists; (2) low need of data; and (3) fast deployment. Specifically, specialist dependency is eliminated by the Virtual Expert Team (VET). The VET collaborates with four virtual experts, including a Knowledge Retriever, Modeler, Coder, and Debugger, to emulate a human expert team for the VDS transferring workflow. These experts specialize in the domain of terminal VDS via a few-shot example learning approach. Through this approach, the experts are able to learn VDS-domain knowledge from a few VDS examples. These examples are retrieved via a Retrieval-Augmented Generation (RAG) mechanism, mitigating the high demand for terminal-specific data. Furthermore, an automatic VDS design workflow is established among these experts to avoid extra manual interventions. In this workflow, a self-correction loop inspired by the LLM Reflexion framework is created
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