Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning

Jens Heger and Thomas Voss

International Journal of Production Research, 2023, vol. 61, issue 1, 147-161

Abstract: 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%.

Date: 2023
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DOI: 10.1080/00207543.2021.1943762

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