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Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System

Research output: Journal contributions › Conference article in journal › Research › peer-review

Standard

Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System. / Voß, Thomas; Heger, Jens; Zein El Abdine, Mazhar.
In: Simulation Notes Europe, Vol. 32, No. 3, 09.2022, p. 169-175.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Harvard

Voß, T, Heger, J & Zein El Abdine, M 2022, 'Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System', Simulation Notes Europe, vol. 32, no. 3, pp. 169-175. https://doi.org/10.11128/sne.32.tn.10617

APA

Voß, T., Heger, J., & Zein El Abdine, M. (2022). Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System. Simulation Notes Europe, 32(3), 169-175. https://doi.org/10.11128/sne.32.tn.10617

Vancouver

Voß T, Heger J, Zein El Abdine M. Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System. Simulation Notes Europe. 2022 Sept;32(3):169-175. doi: 10.11128/sne.32.tn.10617

Bibtex

@article{b447c970899c4667a271da831f475881,
title = "Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System",
abstract = "Integrating machine learning methods into the scheduling process to adjust priority rules dynamically can improve the performance of manufacturing systems. In this paper, three methods for adjusting the k-values of the ATCS sequencing rule are analyzed: neural networks, decision trees and reinforcement learning. They are evaluated in a static and a dynamic scenario. The required dataset was synthetically generated using a discrete event simulation of a flow shop environment, where product mix and system utilization were varied systematically. Across all scenarios, it is shown that all three methods can improve the performance. On par, RL and NN can reduce the mean tardiness by up to 15% and compensate for unplanned product mix changes",
keywords = "Engineering",
author = "Thomas Vo{\ss} and Jens Heger and {Zein El Abdine}, Mazhar",
note = "Special Issue ASIM SPL 2021; 19. Fachtagung {"}Simulation in Produktion und Logistik 2021{"} ; Conference date: 15-09-2021 Through 17-09-2021",
year = "2022",
month = sep,
doi = "10.11128/sne.32.tn.10617",
language = "English",
volume = "32",
pages = "169--175",
journal = "Simulation Notes Europe",
issn = "2305-9974",
publisher = "ARGESIM Verlag ",
number = "3",
url = "http://www.asim-fachtagung-spl.de/asim2021/de/index.html",

}

RIS

TY - JOUR

T1 - Using Decision Trees and Reinforcement Learning for the Dynamic Adjustment of Composite Sequencing Rules in a Flexible Manufacturing System

AU - Voß, Thomas

AU - Heger, Jens

AU - Zein El Abdine, Mazhar

N1 - Conference code: 19

PY - 2022/9

Y1 - 2022/9

N2 - Integrating machine learning methods into the scheduling process to adjust priority rules dynamically can improve the performance of manufacturing systems. In this paper, three methods for adjusting the k-values of the ATCS sequencing rule are analyzed: neural networks, decision trees and reinforcement learning. They are evaluated in a static and a dynamic scenario. The required dataset was synthetically generated using a discrete event simulation of a flow shop environment, where product mix and system utilization were varied systematically. Across all scenarios, it is shown that all three methods can improve the performance. On par, RL and NN can reduce the mean tardiness by up to 15% and compensate for unplanned product mix changes

AB - Integrating machine learning methods into the scheduling process to adjust priority rules dynamically can improve the performance of manufacturing systems. In this paper, three methods for adjusting the k-values of the ATCS sequencing rule are analyzed: neural networks, decision trees and reinforcement learning. They are evaluated in a static and a dynamic scenario. The required dataset was synthetically generated using a discrete event simulation of a flow shop environment, where product mix and system utilization were varied systematically. Across all scenarios, it is shown that all three methods can improve the performance. On par, RL and NN can reduce the mean tardiness by up to 15% and compensate for unplanned product mix changes

KW - Engineering

UR - https://www.sne-journal.org/sne-volumes/volume-32/sne-323-september-2022

UR - https://www.mendeley.com/catalogue/09fba1e1-531d-3cdb-99ab-544f873ade10/

U2 - 10.11128/sne.32.tn.10617

DO - 10.11128/sne.32.tn.10617

M3 - Conference article in journal

VL - 32

SP - 169

EP - 175

JO - Simulation Notes Europe

JF - Simulation Notes Europe

SN - 2305-9974

IS - 3

T2 - 19. Fachtagung "Simulation in Produktion und Logistik 2021"

Y2 - 15 September 2021 through 17 September 2021

ER -

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Research output: Contributions to collected editions/works › Published abstract in conference proceedings › Transfer › peer-review

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