基于深度学习神经网络和量子遗传算法的柔性作业车间动态调度

针对柔性作业车间动态调度问题构建以平均延期惩罚、能耗、偏差度为目标的动态调度优化模型，提出一种基于深度Q学习神经网络的量子遗传算法。首先搭建基于动态事件扰动和周期性重调度的学习环境，利用深度Q学习神经网络算法，建立环境■行为评价神经网络模型作为优化模型的适应度函数。然后利用改进的量子遗传算法求解动态调度优化模型。该算法设计了基于工序编码和设备编码的多层编码解码方案；制定了基于适应度的动态调整旋转角策略，提高了种群的收敛速度；结合基于Tent映射的混沌搜索算法，以跳出局部最优解。最后通过测试算例验证了环境-行为评价神经网络模型的鲁棒性和对环境的适应性，以及优化算法的有效性。

Dynamic scheduling of flexible job shop based on deep Q-learning neural network and quantum genetic algorithm

To deal with the problem of dynamic scheduling of flexible job shop, a dynamic scheduling optimization model was constructed to minimize average delay penalty, energy consumption and deviation, and an ameliorated quantum genetic algorithm based on deep Q-learning neural network was proposed. First, a learning environment based on dynamic event disturbance and periodic rescheduling was built, and an environment-behavior evaluation neural network model was established using deep Q-learning neural network algorithm as the fitness function of the optimization model. Then the dynamic scheduling optimization model was solved by using the improved quantum genetic algorithm which designed a multi-layer encoding and decoding scheme based on process encoding and equipment encoding. A strategy for dynamically adjusting the rotation angle based on fitness was developed to improve the convergence speed of the population and exclude local solutions by combining with chaos-based Tent mapping search. Finally, test cases verified the robustness and adaptability of the environment-behavior evaluation neural network model, as well as the effectiveness of the optimization algorithm.