Robust Scheduling Based on Deep Reinforcement Learning for Flexible Job Shop With Machine Breakdown and New Job Arrival

Abstract With the continuous growth of personalized product demands and the upsurge in new customer orders, the arrival of new jobs and machine breakdowns due to overloading have emerged as common production disturbances, inevitably affecting the production plan. To maintain the stability of the scheduling for dynamic flexible job shops with machine breakdown and new job arrival, this article proposes a robust scheduling method that is designed with a flexible network structure and dual-action chained cooperative decision-making mechanism based on deep reinforcement learning (FD-DRL). First, a flexible neural network structure is innovatively constructed, which embeds the feature vector into operation nodes to design a dynamic production state extraction method with graph neural networks (GNN). Second, the dual-action chained cooperative decision-making mechanism is established for agents, who consider the new and remaining operations overall to maximize the utilization of machine idle time. Finally, through training and verification, the effectiveness and advancement of the proposed FD-DRL method are verified by comparing with heuristic/meta-heuristics and the static model of deep reinforcement learning (DRL).