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