Show simple item record

dc.contributor.authorvan Staden, Heletjé E.
dc.contributor.authorDeprez, Laurens
dc.contributor.authorBoute, Robert
dc.date.accessioned2022-01-31T07:03:50Z
dc.date.available2022-01-31T07:03:50Z
dc.date.issued2022en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2022.01.037
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7000
dc.description.abstractWe investigate whether historical machine failures and maintenance records may be used to derive future machine failure estimates and, in turn, prescribe advancements of scheduled preventive maintenance interventions. We model the problem using a sequential predict, then optimize approach. In our prescriptive optimization model, we use a finite horizon Markov decision process with a variable order Markov chain, in which the chain length varies depending on the time since the last preventive maintenance action was performed. The model therefore captures the dependency of a machine’s failures on both recent failures as well as preventive maintenance actions, via our prediction model. We validate our model using an original equipment manufacturer data set and obtain policies that prescribe when to deviate from the planned periodic maintenance schedule. To improve our predictions for machine failure behavior with limited to no past data, we pool our data set over different machine classes by means of a Poisson generalized linear model. We find that our policies can supplement and improve on those currently applied by 5%, on average.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectMaintenanceen_US
dc.subjectData-driven Decision Makingen_US
dc.subjectMarkov Decision Processen_US
dc.subjectData Poolingen_US
dc.titleA dynamic “predict, then optimize” preventive maintenance approach using operational intervention dataen_US
dc.identifier.journalEuropean Journal of Operational Researchen_US
dc.source.volume302
dc.source.issue3
dc.source.beginpage1079
dc.source.endpage1096
dc.contributor.departmentUniversity College Dublin, School of Business, Carysfort Ave, Carrysfort, Blackrock, Co. Dublin, Irelanden_US
dc.contributor.departmentUniversity of Luxembourg, Luxembourg Centre for Logistics and Supply Chain Management, Faculty of Law, Economics and Finance, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourgen_US
dc.contributor.departmentKU Leuven, Faculty of Economics and Business, Naamsestraat 69, Box 3555, Leuven, 3000, Belgiumen_US
dc.contributor.departmentFlanders Make, VCCM, Gaston Geenslaan 8, 3001, Heverlee, Belgiumen_US
dc.identifier.eissn1872-6860
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleVlerick strategic journal articleen_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid219312en_US
dc.identifier.vperid102358en_US


Files in this item

Thumbnail
Name:
Publisher version

This item appears in the following Collection(s)

Show simple item record