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

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Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning. / Heger, Jens; Voss, Thomas.
in: International Journal of Production Research, Jahrgang 61, Nr. 1, 2023, S. 147-161.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{8844f6454dae4ae29c469eb951925239,
title = "Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning",
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%.",
keywords = "Engineering, Sequencing rules, dynamic adjustment, simulation study, reinforcement learning, production planning and control",
author = "Jens Heger and Thomas Voss",
note = "Publisher Copyright: {\textcopyright} 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",
year = "2023",
doi = "10.1080/00207543.2021.1943762",
language = "English",
volume = "61",
pages = "147--161",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Routledge Taylor & Francis Group",
number = "1",

}

RIS

TY - JOUR

T1 - Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning

AU - Heger, Jens

AU - Voss, Thomas

N1 - 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

PY - 2023

Y1 - 2023

N2 - 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%.

AB - 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%.

KW - Engineering

KW - Sequencing rules

KW - dynamic adjustment

KW - simulation study

KW - reinforcement learning

KW - production planning and control

UR - http://www.scopus.com/inward/record.url?scp=85109310848&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/4063b450-6da4-3b5a-918f-b6cda7cf7e07/

U2 - 10.1080/00207543.2021.1943762

DO - 10.1080/00207543.2021.1943762

M3 - Journal articles

VL - 61

SP - 147

EP - 161

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 1

ER -

DOI