用大模型提升制造系统中资源动态探索能力，应对突发变化。
A Large Language Model-Enabled Control Architecture for Dynamic Resource Capability Exploration in Multi-Agent Manufacturing Systems
- 用大语言模型实现多智能体系统的上下文感知决策
- 仿真显示吞吐量提升，资源利用率更高效
- 适合需要实时响应的智能制造场景
制造环境因需求波动和产品生命周期缩短而日益复杂且不可预测，亟需实时决策与扰动适应能力。传统控制方法在动态工业场景中响应迟缓，难以应对突发挑战。多智能体系统通过去中心化决策提升响应能力，但现有方案在实时适应、上下文感知决策及资源能力动态探索方面仍存局限。大语言模型具备上下文理解能力，可突破上述瓶颈。本文提出一种基于大语言模型的多智能体制造系统控制架构，实现对资源能力的动态探索以应对实时扰动。基于仿真的案例研究显示，该架构显著提升系统韧性和灵活性，相比现有方法，在吞吐量和资源利用效率方面均有改善。
原文摘要 · Abstract (English)
Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they demonstrate limitations in responsiveness within dynamic industrial settings. Multi-agent systems address these challenges through decentralization of decision-making, enabling systems to respond dynamically to operational changes. However, current multi-agent systems encounter challenges related to real-time adaptation, context-aware decision-making, and the dynamic exploration of resource capabilities. Large language models provide the possibility to overcome these limitations through context-aware decision-making capabilities. This paper introduces a large language model-enabled control architecture for multi-agent manufacturing systems to dynamically explore resource capabilities in response to real-time disruptions. A simulation-based case study demonstrates that the proposed architecture improves system resilience and flexibility. The case study findings show improved throughput and efficient resource utilization compared to existing approaches.
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