Disclosed is a temporal difference-based deep reinforcement learning algorithm, which is used to solve a hybrid flow-shop scheduling problem of a related parallel machines. The algorithm combines a convolutional neural network with TD learning in reinforcement learning, and performs behavior selection according to inputted state features so as to more fully comply with a scheduling decision-making process of an actual order response-type production and fabrication system. By means of transforming the scheduling problem into a multi-stage decision-making problem, a convolutional neural network model is used to fit a state value function, fabrication system processing state feature data is inputted into the model, a temporal difference method is used to train the model, a heuristic algorithm or an allocation rule is used as candidate scheduling decision behavior, and an optimal combined behavior strategy is selected for each scheduling decision by combining with a reinforcement learning reward and punishment mechanism. Compared to the prior art, the algorithm proposed by the present invention has the advantages of strong real-time performance and high flexibility.
TEMPORAL DIFFERENCE-BASED HYBRID FLOW-SHOP SCHEDULING METHOD