Deep reinforcement learning optimization scheduling algorithm for continuous production line
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.
deep reinforcement learningassembly line productionscheduling optimizationiterative greedy algorithmdata dimension reduction
万方数据
维普
