Abstract As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters cannot be dynamically adjusted effectively during the calculation process, which causes the solution efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the self-learning model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as the learning methods at initial and later stages of optimization, respectively, and the conversion condition is designed. Secondly, the state determination method and reward method are designed for RL in GA environment. Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA significantly outperforms its competitors in solving FJSP.

中文翻译：

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基于强化学习的柔性作业车间调度问题的自学习遗传算法

摘要 作为生产调度的一个重要分支，柔性作业车间调度问题（FJSP）是一个难以解决的问题，被证明是NP-hard问题。已经提出了许多求解FJSP的智能算法，但它们的关键参数在计算过程中不能有效地动态调整，导致求解效率和质量不能满足生产要求。为此，本文提出了一种以遗传算法(GA)为基本优化方法，并基于强化学习(RL)智能调整其关键参数的自学习遗传算法(SLGA)。首先在SLGA中分析构建自学习模型，在优化的初期和后期采用SARSA算法和Q-Learning算法作为学习方法，分别设计了转换条件。其次，针对 GA 环境中的 RL 设计了状态确定方法和奖励方法。最后，使用两组不同尺度的基准数据实例，将SLGA在求解FJSP中的学习效果和性能与其他算法进行比较。实验结果表明，所提出的 SLGA 在解决 FJSP 方面明显优于其竞争对手。