A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor
CoRR(2024)
摘要
As productivity advances, the demand of customers for multi-variety and
small-batch production is increasing, thereby putting forward higher
requirements for manufacturing systems. When production tasks frequent changes
due to this demand, traditional manufacturing systems often cannot response
promptly. The multi-agent manufacturing system is proposed to address this
problem. However, because of technical limitations, the negotiation among
agents in this kind of system is realized through predefined heuristic rules,
which is not intelligent enough to deal with the multi-variety and small batch
production. To this end, a Large Language Model-based (LLM-based) multi-agent
manufacturing system for intelligent shopfloor is proposed in the present
study. This system delineates the diverse agents and defines their
collaborative methods. The roles of the agents encompass Machine Server Agent
(MSA), Bid Inviter Agent (BIA), Bidder Agent (BA), Thinking Agent (TA), and
Decision Agent (DA). Due to the support of LLMs, TA and DA acquire the ability
of analyzing the shopfloor condition and choosing the most suitable machine, as
opposed to executing a predefined program artificially. The negotiation between
BAs and BIA is the most crucial step in connecting manufacturing resources.
With the support of TA and DA, BIA will finalize the distribution of orders,
relying on the information of each machine returned by BA. MSAs bears the
responsibility for connecting the agents with the physical shopfloor. This
system aims to distribute and transmit workpieces through the collaboration of
the agents with these distinct roles, distinguishing it from other scheduling
approaches. Comparative experiments were also conducted to validate the
performance of this system.
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