Deep reinforcement learning optimization scheduling algorithm for continuous production line

In order to improve the scheduling effect of continuous production lines and enhance the processing efficiency of production lines, a deep reinforcement learning optimization scheduling algorithm for continuous production lines is proposed. The Monte Carlo algorithm and Bayesian evaluation method are combined to reduce the data complexity of the continuous production line pipeline problem; the deep neural network model is used to optimize the pipeline scheduling parameters, evaluate and encode them; the iterative greedy algorithm is combined with the deep reinforcement learning method to implement the model solution for the scheduling data problem and realize the scheduling of continuous production lines. The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are all higher than 0.9531, the process delay is optimized to less than 5 min, the collection speed is fast, and the processing efficiency of the production line is improved.