A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor

As customer demand for multi-variety and small-batch production increases, dynamic disturbances place greater demands on manufacturing systems. To address such challenges, researchers proposed the multi-agent manufacturing system. However, conventional agent negotiation typically relies on pre-defined and fixed heuristic rules, which are ill-suited to managing complex and fluctuating disturbances. In current implementations, mainstream approaches based on reinforcement learning require the development of simulators and training models specific to a given shopfloor, necessitating substantial computational resources and lacking scalability. To overcome this limitation, the present study proposes a Large Language Model-based (LLM-based) multi-agent manufacturing system for intelligent shopfloor management. By defining the diverse modules of agents and their collaborative methods, this system facilitates the processing of all workpieces with minimal human intervention. The agents in this system consist of the Machine Server Module (MSM), Bid Inviter Module (BIM), Bidder Module (BM), Thinking Module (TM), and Decision Module (DM). By harnessing the reasoning capabilities of LLMs, these modules enable agents to dynamically analyze shopfloor information and select appropriate processing machines. The LLM-based modules, predefined by system prompts, provide dynamic functionality for the system without the need for pre-training. Extensive experiments were conducted in physical shopfloor settings. The results demonstrate that the proposed system exhibits strong adaptability, and achieves superior performance (makespan) and stability (as measured by sample standard deviation) compared to other approaches without requiring pre-training.