<div class="csl-bib-body">
<div class="csl-entry">Faustmann, G. (2019). <i>Application of machine learning in production scheduling</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.68471</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.68471
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/6541
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dc.description.abstract
For human experts, it is often too hard or too time-consuming to manually detect patterns in big data sets. Machine learning is applied in many areas to detect such patterns. Its applications are by no means limited to research, as machine learning also plays a big role in the industrial sector. This thesis applies machine learning to the following two topics. The first part of the thesis deals with product quality classification for automotive paint shops. The second part investigates automated parameter configuration for dispatching rules that are used in machine scheduling. We use a binary classification to predict the product quality of an automotive paint shop based on its scheduling data. We propose a set of features to characterize the production process. These features are used to classify whether or not the quality of the product is satisfactory. We can show that the best model we found performs better than a baseline model on an unseen data set. In the automated parameter configuration part of the thesis, we investigate machine learning methods based on multi-target regression to automatically configure dispatching rules for real-life planning scenarios where multiple objectives are considered. We propose a novel set of features to characterize instances of the parallel machine scheduling problem, and describe how supervised learning can be used to obtain optimized parameter configurations for given machine scheduling instances. Experimental results show that our approach can obtain high-quality solutions for real-life scheduling scenarios in short run times.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Machine Learning
en
dc.subject
Production Scheduling
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dc.subject
/ Product Quality Prediction
en
dc.subject
Dispatching Rules
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dc.subject
Automated Algorithm Configuration
en
dc.subject
Machine Learning
de
dc.subject
Production Scheduling
de
dc.subject
Product Quality Prediction
de
dc.subject
Dispatching Rules
de
dc.subject
Automated Algorithm Configuration
de
dc.title
Application of machine learning in production scheduling
en
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.68471
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Georg Faustmann
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E192 - Institut für Logic and Computation
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC15530614
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dc.description.numberOfPages
61
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-131652
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0002-3992-8637
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item.fulltext
with Fulltext
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.languageiso639-1
en
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openaccessfulltext
Open Access
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item.openairetype
master thesis
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item.grantfulltext
open
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crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence