Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning
针对柔性流水车间排序难题，提出用强化学习作为超启发式方法动态调整ATCS规则的k值，在不同产品组合和利用率下训练并比较，能将平均延迟降低最多5%。
Given the fact that finding the optimal sequence in a flexible flow shop is usually an NP-hard problem, priority-based sequencing rules are applied in many real-world scenarios. In this contribution, an innovative reinforcement learning approach is used as a hyper-heuristic to dynamically adjust the k-values of the ATCS sequencing rule in a complex manufacturing scenario. For different product mixes as well as different utilisation levels, the reinforcement learning approach is trained and compared to the k-values found with an extensive simulation study. This contribution presents a human comprehensible hyper-heuristic, which is able to adjust the k-values to internal and external stimuli and can reduce the mean tardiness up to 5%.