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

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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%.
Original languageEnglish
JournalInternational Journal of Production Research
Issue number1
Pages (from-to)147-161
Number of pages15
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Titel der Ausgabe: Analytics and Machine Learning in Scheduling and Routing Optimization

    Research areas

  • Engineering - Sequencing rules, dynamic adjustment, simulation study, reinforcement learning, production planning and control