Abstract: Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automotive manufacturing,as it ensures efficient production operations.Typically,the main-manufacturer formulates an annualinventory management plan and contacts suppliers when certain materials approach critical inventory levels based on the actual production schedule.However,changes in actual production conditions may necessitate alterations to the annual inventory ma-nagement plan.Therefore,making procurement decisions based on actual production conditions and inventory is relatively moreflexible and efficient.In recent years,many researchers have focused on using reinforcement learning methods to study inventory management problems.Current methods can achieve a certain degree of efficient management when solving the inventory management problem in the civil aircraft manufacturing supply chain with a multi-node and multi-material model,but with high complexity.To address this issue,we formalize the problem as a partially observable Markov decision process model and propose a multi-agent supply chain inventory management method based on improved transformer.This method transforms the multi-agent reinforcement learning problem into a sequence modeling problem with an encoder-decoder architecture based on the essence of multi-agent reinforcement learning sequence decision-making,logically reducing the complexity of the algorithm.Experimental results show that compared to existing reinforcement learning-based methods,the proposed method has about 90% improvement in complexity while maintaining similar performance.

Key words: Multi-agent reinforcement learning, Aircraft supply chain inventory management, Partially observable Markov decision process, Transformer