Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automobile manufacturing to ensure efficient manufacturing. Generally, the main manufacturer makes the annual inventory management plan, and contacts with suppliers when some material is approaching critical inventory level according to the actual production schedule, which increases the difficulty of inventory management. In recent years, many researchers have focused on using reinforcement learning method to study inventory management problems. Current approaches were mainly designed for the supply chain with single-node multi-material or multi-node single-material mode, which are not suitable to the civil aircraft manufacturing supply chain with multi-node multi-material mode. To deal with this problem, we formulated the problem as a partially observable Markov decision process (POMDP) model and proposed a multi-agent reinforcement learning method for supply chain inventory management, in which the dual-policy and information transmission mechanism was designed to help the supply chain participant improve the global information utilization efficiency of the supply chain and the coordination efficiency with other participants. The experiment results show that our method has about 45% performance improvement on efficiency compared with current reinforcement learning-based methods.

The production of civil aircrafts is confronted with a significant demand for the interconnectivity of production resources among distributed factories, while the complex coupling relationships among various production resources might restrict the improvement of production efficiency. Therefore, researching scheduling methods for civil aircraft distributed production is necessary, but previous studies have not taken material inventory into account sufficiently. This article proposes a scheduling method for civil aircraft distributed production that aims to minimize the production time to complete all the jobs in a large production station under the condition of material inventory replenishment. Firstly, we analyze the factors constraining civil aircraft production efficiency, and formulize the production scheduling problem into the Resource-Constrained Project Scheduling Problem model with Inventory Replenishment (RCPSP-IR). Precedence constraints and resource constraints, especially the inventory constraints, are mainly considered in RCPSP-IR. To solve the corresponding scheduling problem, the Genetic Algorithm (GA) is applied and multiple approaches are introduced to handle the complex constraints and avoid local optimum. Finally, we applied the proposed scheduling method to a case study of a jet twin-engine civil aircraft production of COMAC. The results of the case study show that the proposed method can give a nearly optimal scheduling strategy to be applied to actual civil aircraft production.