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

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

Other publications by the same author(s)

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

Müller, K. & Heger, J., 2025, Simulation in Produktion und Logistik 2025. Rank, S., Kühn, M. & Schmidt, T. (eds.). Dresden: Dresden University of Technology, 10 p. 40. (ASIM-Mitteilung; no. 194)(Tagungsband ASIM-Fachtagung Simulation in Produktion und Logistik; vol. 21).

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

Enhancing SME Production Efficiency: A Case Study on Autonomous Manufacturing Systems

Prüfer, O. C. & Heger, J., 2025, In: Procedia CIRP. 134, p. 313-318 6 p.

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

Manufacturing of irregular shapes through force control in incremental sheet forming with active medium

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

Rethink Textile Production - Developing sustainable concepts for textile industry using production simulation

Müller, K., Guerne, M. G., Block, B.-M. & Heger, J., 04.2025, 14th International Materials Education Symposium: Clare College, University of Cambridge Cambridge, UK April 10-11, 2025. Canonsburg: University of Cambridge, p. 27 1 p. ( Materials Education Symposium).

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

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