Research on intelligent scheduling method for EMU train high-level maintenance

Traditional high-level maintenance work organization of EMU mainly relies on manual scheduling, which has problems such as low efficiency and difficulty in inheritance. In order to improve the efficiency of maintenance operations, a scheduling model and scheduling optimization algorithm based on artificial intelligence for EMU Train High-level Maintenance are designed. On the basis of traditional workshop scheduling optimization, taking into account the order relationship of production processes, the high-level maintenance scheduling optimization model of EMU is established with the constraints of maintenance process flow. In view of the characteristics of complex order relationship constraints in the model, a smart optimization algorithm suitable for high-level maintenance scheduling of EMU is designed. Through case analysis of high-level maintenance operations in a certain domestic EMU depot, the effectiveness of the model and algorithm is verified. The results show that the intelligent maintenance process adopted in this paper can effectively reduce the maintenance time in the high-level maintenance depot of EMU, Compared with the traditional maintenance process, it can save 17.6% of the maintenance time, and at the same time significantly increase the number of synchronous maintenance tasks for high-level maintenance of EMU, thereby improving the efficiency of maintenance.