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

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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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%.
OriginalspracheEnglisch
ZeitschriftINTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Anzahl der Seiten15
ISSN0020-7543
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung - 01.07.2021

DOI