Publication:
A machine learning approach for implementing data-driven production control policies

dc.contributor.departmentDepartment of Business Administration
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorKhayyati, Siamak
dc.contributor.kuauthorTan, Barış
dc.contributor.schoolcollegeinstituteCollege of Administrative Sciences and Economics
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T13:09:38Z
dc.date.issued2021
dc.description.abstractGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue10
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuEU - TÜBİTAK
dc.description.sponsorshipEuropean Union (EU)
dc.description.sponsorshipHorizon 2020
dc.description.sponsorshipEU ECSEL Joint Undertaking
dc.description.sponsorshipProject Productive4.0
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TÜBİTAK)
dc.description.versionAuthor's final manuscript
dc.description.volume60
dc.identifier.doi10.1080/00207543.2021.1910872
dc.identifier.eissn1366-588X
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR03177
dc.identifier.issn0020-7543
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85104322442
dc.identifier.urihttps://hdl.handle.net/20.500.14288/2768
dc.identifier.wos639781700001
dc.keywordsDiscrete event simulation
dc.keywordsMachine learning
dc.keywordsProduction control
dc.keywordsReal-time control
dc.keywordsStochastic models
dc.language.isoeng
dc.publisherTaylor _ Francis
dc.relation.grantno737459
dc.relation.grantno217M145
dc.relation.ispartofInternational Journal of Production Research
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/9797
dc.subjectEngineering
dc.subjectOperations research
dc.subjectManagement science
dc.titleA machine learning approach for implementing data-driven production control policies
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorTan, Barış
local.contributor.kuauthorKhayyati, Siamak
local.publication.orgunit1College of Administrative Sciences and Economics
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Department of Business Administration
local.publication.orgunit2Graduate School of Sciences and Engineering
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