Application of data mining in scheduling of single machine system

dc.contributor.advisor Sigurdur Olafsson
dc.contributor.author Li, Xiaonan
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.contributor.other Industrial and Manufacturing Systems Engineering
dc.date 2018-08-24T23:15:48.000
dc.date.accessioned 2020-06-30T07:44:08Z
dc.date.available 2020-06-30T07:44:08Z
dc.date.issued 2006-01-01
dc.description.abstract <p>The rapidly growing field of data mining has the potential of improving performance of existing scheduling systems. Such systems generate large amounts of data, which is often not utilized to its potential. The problem is whether it is possible to discover the implicit knowledge behind scheduling practice and then, with this knowledge, we could improve current scheduling practice. In this dissertation, we propose a novel methodology for generating scheduling rules using a data-driven approach. We show how to use data mining to discover previously unknown dispatching rules by applying the learning algorithms directly to production data. We also consider how by using this new approach unexpected knowledge and insights can be obtained, in a manner that would not be possible if an explicit model of the system or the basic scheduling rules had to be obtained beforehand. However, direct data mining of production data can at least mimic scheduling practices. The problem is whether scheduling practice could be improved with the knowledge discovered by data mining. We propose to combine data mining with optimization for effective production. In this approach, we use a genetic algorithm to find a heuristic solution to the optimal instances selection problem, and then induce a decision tree from this subset of instances. The optimal instance selection can be viewed as determining the best practices from what has been done in the past, and the data mining can then learn new dispatching rules from those best practices.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/1539/
dc.identifier.articleid 2538
dc.identifier.contextkey 6094976
dc.identifier.doi https://doi.org/10.31274/rtd-180813-12131
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/1539
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/69017
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/1539/r_3229100.pdf|||Fri Jan 14 20:40:08 UTC 2022
dc.subject Industrial and manufacturing systems engineering
dc.subject Industrial engineering
dc.title Application of data mining in scheduling of single machine system
dc.type dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
thesis.degree.level Doctor of Philosophy
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