In order to improve the scheduling effect of the continuous production line and improve the processing efficiency of the production line,a deep reinforcement learning optimization scheduling algorithm for the continuous production line is proposed.Combining Monte Carlo algorithm and Bayesian evaluation method to reduce the data complexity of the continuous production line problem;A deep neural network model is used to optimize the pipeline scheduling parameters,evaluate and code them;The iterative greedy algorithm is combined with the deep reinforcement learning method to solve the scheduling data problem and realize the continuous production line scheduling.The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are higher than 0.9531,and the process delay is optimized to less than 5 min,which improves the processing efficiency of the production line.
关键词
深度强化学习/流水线生产/调度优化/迭代贪婪算法/数据降维
Key words
deep reinforcement learning/assembly line production/scheduling optimization/iterative greedy algorithm/data dimension reduction
维普
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