Switching Dispatching Rules with Gaussian Processes
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Robust Manufacturing Control: Proceedings of the CIRP Sponsored Conference RoMaC 2012. ed. / Katja Windt. Springer Verlag, 2013. p. 91-103 (Lecture Notes in Production Engineering; Vol. Part F1157).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Switching Dispatching Rules with Gaussian Processes
AU - Heger, Jens
AU - Hildebrandt, Torsten
AU - Scholz-Reiter, Bernd
N1 - Publisher Copyright: © 2013, Springer-Verlag Berlin Heidelberg.
PY - 2013
Y1 - 2013
N2 - Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems, e.g. semiconductor manufacturing. Nevertheless, no dispatching rule outperforms other rules across various objectives, scenarios and system conditions. In this paper we present an approach to dynamically select the most suitable rule for the current system conditions in real time. We calculate Gaussian process (GP) regression models to estimate each rule’s performance and select the most promising one. The data needed to create these models is gained by a few preliminary simulation runs of the selected job shop scenario from the literature. The approach to use global information to create the Gaussian process models leads to better local decision at the machine level. Using a dynamic job shop scenario we demonstrate, that our approach is capable of significantly reducing the mean tardiness of jobs.
AB - Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems, e.g. semiconductor manufacturing. Nevertheless, no dispatching rule outperforms other rules across various objectives, scenarios and system conditions. In this paper we present an approach to dynamically select the most suitable rule for the current system conditions in real time. We calculate Gaussian process (GP) regression models to estimate each rule’s performance and select the most promising one. The data needed to create these models is gained by a few preliminary simulation runs of the selected job shop scenario from the literature. The approach to use global information to create the Gaussian process models leads to better local decision at the machine level. Using a dynamic job shop scenario we demonstrate, that our approach is capable of significantly reducing the mean tardiness of jobs.
KW - Engineering
KW - Simulation
KW - Gaussian process regression
KW - Scheduling
KW - Dispatching rules
UR - http://www.scopus.com/inward/record.url?scp=85166678672&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/4da6c216-d6e8-3d93-9739-bb6c3af812f1/
U2 - 10.1007/978-3-642-30749-2_7
DO - 10.1007/978-3-642-30749-2_7
M3 - Article in conference proceedings
SN - 978-3-642-30748-5
T3 - Lecture Notes in Production Engineering
SP - 91
EP - 103
BT - Robust Manufacturing Control
A2 - Windt, Katja
PB - Springer Verlag
T2 - Conference on Robust Manufacturing Control - RoMaC 2012
Y2 - 18 June 2012 through 20 June 2012
ER -