A Deep Reinforcement Learning Approach to Flexible Job Shop Scheduling

F1exible job shop scheduling (FJSP) is one of the most important problems in the domain of machining process optimization. This paper proposes a deep reinforcement learning approach to resolve the FJSP. In the approach, the FJSP is formulated as a Markov decision process where disjunctive graph is used to represent the state, operation set and machine allocation are used as the actions, the reward function is established based on the optimization objective (i.e. makespan). To obtain the embedding representation of the disjunctive graph of the FJSP, the corresponding graph neural network (GNN) is used to extract the state features. The multi-layer perceptron (MLP) decision network and scheduling rules cooperate to achieve the selection of actions (i.e. operations and machines). The multi-threaded asynchronous advantage actor-critic (A3C) algorithm is employed to optimize the model parameters to shorten the training time. The approach has been tested on the benchmarks. The results prove that this approach is superior to scheduling rules and meta-heuristic algorithms in results and computing time respectively.