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Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times. / Heger, Jens; Branke, Jurgen; Hildebrandt, Torsten et al.
In: International Journal of Production Research, Vol. 54, No. 22, 16.11.2016, p. 6812-6824.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Heger, J, Branke, J, Hildebrandt, T & Scholz-Reiter, B 2016, 'Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times', International Journal of Production Research, vol. 54, no. 22, pp. 6812-6824. https://doi.org/10.1080/00207543.2016.1178406

APA

Heger, J., Branke, J., Hildebrandt, T., & Scholz-Reiter, B. (2016). Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times. International Journal of Production Research, 54(22), 6812-6824. https://doi.org/10.1080/00207543.2016.1178406

Vancouver

Heger J, Branke J, Hildebrandt T, Scholz-Reiter B. Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times. International Journal of Production Research. 2016 Nov 16;54(22):6812-6824. doi: 10.1080/00207543.2016.1178406

Bibtex

@article{751bc781cfb1410daaf95f2c58e79765,
title = "Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times",
abstract = "Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.",
keywords = "scheduling, simulation, production, artificial intelligence, flexible manufacturing systems, Gaussian processes, Engineering",
author = "Jens Heger and Jurgen Branke and Torsten Hildebrandt and Bernd Scholz-Reiter",
year = "2016",
month = nov,
day = "16",
doi = "10.1080/00207543.2016.1178406",
language = "English",
volume = "54",
pages = "6812--6824",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",
number = "22",

}

RIS

TY - JOUR

T1 - Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

AU - Heger, Jens

AU - Branke, Jurgen

AU - Hildebrandt, Torsten

AU - Scholz-Reiter, Bernd

PY - 2016/11/16

Y1 - 2016/11/16

N2 - Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.

AB - Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.

KW - scheduling

KW - simulation

KW - production

KW - artificial intelligence

KW - flexible manufacturing systems

KW - Gaussian processes

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84973912681&partnerID=8YFLogxK

U2 - 10.1080/00207543.2016.1178406

DO - 10.1080/00207543.2016.1178406

M3 - Journal articles

VL - 54

SP - 6812

EP - 6824

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 22

ER -

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Other publications by the same author(s)

Einsatz von bestärkendem Lernen in der Reihenfolgeplanung mit dem Ziel der platzeffizienten Produktion

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

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

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Research output: Journal contributions › Journal articles › Research › peer-review

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Thiery, S., Zein El Abdine, M., Heger, J. & Ben Khalifa, N., 01.05.2025, In: Advances in Industrial and Manufacturing Engineering. 10, 11 p., 100157.

Research output: Journal contributions › Journal articles › Research › peer-review

Optimizing dataset design for data-driven models of the deep drawing process using active transfer learning

Heinzel, C., Wollschläger, L., Nurmatov, B.-M., Heger, J. & Khalifa, N. B., 2025, In: Journal of Physics: Conference Series. 3104, 1, 11 p., 012064.

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

DOI

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