Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/100779
DC FieldValueLanguage
dc.contributor.authorAndrade, Pedro-
dc.contributor.authorSilva, Catarina-
dc.contributor.authorRibeiro, Bernardete Martins-
dc.contributor.authorSantos, Bruno F.-
dc.date.accessioned2022-07-11T08:36:57Z-
dc.date.available2022-07-11T08:36:57Z-
dc.date.issued2021-
dc.identifier.issn2226-4310pt
dc.identifier.urihttps://hdl.handle.net/10316/100779-
dc.description.abstractThis paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.pt
dc.language.isoengpt
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/769288/EUpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectaircraft maintenancept
dc.subjectmaintenance check schedulingpt
dc.subjectreinforcement learningpt
dc.subjectq-learningpt
dc.titleAircraft Maintenance Check Scheduling Using Reinforcement Learningpt
dc.typearticle-
degois.publication.firstPage113pt
degois.publication.issue4pt
degois.publication.titleAerospacept
dc.peerreviewedyespt
dc.identifier.doi10.3390/aerospace8040113pt
degois.publication.volume8pt
dc.date.embargo2021-01-01*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0002-7019-7721-
crisitem.author.orcid0000-0002-5656-0061-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais
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This item is licensed under a Creative Commons License Creative Commons