A Deep Reinforcement Learning-based Rescheduling Method for Flexible Job Shops under Machine Breakdowns

This paper considers a flexible job shop rescheduling problem under machine breakdowns to minimize the sum of the deviations from the preschedule. A deep reinforcement learning (DRL)-based rescheduling method is proposed for the problem. Total slack transmission graph is proposed as the input of the deep neural network and graph attention network is adopted to extract the features of the graph. Operation selection network and machine selection network are designed to perform a series of actions with the aim of repairing the preschedule that affected by a machine breakdown. Especially, a "done" network is designed to stop the action selections. The numerical results show that the proposed DRL-based rescheduling method is effective with comparisons of reactive recovery rescheduling heuristics.