A multi-agent reinforcement learning based scheduling strategy for flexible job shops under machine breakdowns

In a highly disrupted workshop environment, machine failures may occur frequently, requiring real-time schedule repair strategies. This paper proposes a type-aware multi-agent deep reinforcement learning (MADRL) to address real-time schedule repair for the flexible job shop scheduling problem under machine breakdowns. First, the problem is modeled as a multi-agent Markov decision process. At each decision point, the relationships among machine …