The manufacturing industry is undergoing a transformative shift, driven by cutting-edge technologies like 5G, AI, and cloud computing. Despite these advancements, effective system control, which is crucial for optimizing production efficiency, remains a complex challenge due to the intricate, knowledge-dependent nature of manufacturing processes and the reliance on domain-specific expertise. Conventional control methods often demand heavy customization, considerable computational resources, and lack transparency in decision-making. In this work, we investigate the feasibility of using Large Language Models (LLMs), particularly GPT-4, as a straightforward, adaptable solution for controlling manufacturing systems, specifically, mobile robot scheduling. We introduce an LLM-based control framework to assign mobile robots to different machines in robot assisted serial production lines, evaluating its performance in terms of system throughput. Our proposed framework outperforms traditional scheduling approaches such as First-Come-First-Served (FCFS), Shortest Processing Time (SPT), and Longest Processing Time (LPT). While it achieves performance that is on par with state-of-the-art methods like Multi-Agent Reinforcement Learning (MARL), it offers a distinct advantage by delivering comparable throughput without the need for extensive retraining. These results suggest that the proposed LLM-based solution is well-suited for scenarios where technical expertise, computational resources, and financial investment are limited, while decision transparency and system scalability are critical concerns.

翻译：制造业正在经历一场由5G、人工智能和云计算等尖端技术驱动的变革性转型。尽管取得了这些进步，但由于制造过程本身具有复杂且依赖知识的特性，以及对领域专业知识的依赖，作为优化生产效率关键环节的有效系统控制仍然是一项复杂的挑战。传统的控制方法通常需要大量定制化、可观的算力资源，并且决策过程缺乏透明度。在本研究中，我们探讨了使用大语言模型（特别是GPT-4）作为控制制造系统（具体而言是移动机器人调度）的一种直接、适应性强的解决方案的可行性。我们提出了一种基于LLM的控制框架，用于在机器人辅助的串行生产线中为移动机器人分配不同的机器，并以系统吞吐量为指标评估其性能。我们提出的框架在性能上超越了传统调度方法，如先到先服务（FCFS）、最短处理时间（SPT）和最长处理时间（LPT）。虽然其性能与多智能体强化学习（MARL）等先进方法相当，但它具有一个显著优势：无需大量重新训练即可实现可比的吞吐量。这些结果表明，所提出的基于LLM的解决方案非常适合技术专长、计算资源和资金投入有限，而决策透明度和系统可扩展性又至关重要的应用场景。