Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/111189
DC Field | Value | Language |
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dc.contributor.author | Silva, Catarina | - |
dc.contributor.author | Andrade, Pedro | - |
dc.contributor.author | Ribeiro, Bernardete M. | - |
dc.contributor.author | F Santos, Bruno | - |
dc.date.accessioned | 2024-01-04T11:54:04Z | - |
dc.date.available | 2024-01-04T11:54:04Z | - |
dc.date.issued | 2023-10-03 | - |
dc.identifier.issn | 2045-2322 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/111189 | - |
dc.description.abstract | This paper proposes using reinforcement learning (RL) to schedule maintenance tasks, which can significantly reduce direct operating costs for airlines. The approach consists of a static algorithm for long-term scheduling and an adaptive algorithm for rescheduling based on new maintenance information. To assess the performance of both approaches, three key performance indicators (KPIs) are defined: Ground Time, representing the hours an aircraft spends on the ground; Time Slack, measuring the proximity of tasks to their due dates; and Change Score, quantifying the similarity level between initial and adapted maintenance plans when new information surfaces. The results demonstrate the efficacy of RL in producing efficient maintenance plans, with the algorithms complementing each other to form a solid foundation for routine tasks and real-time responsiveness to new information. While the static algorithm performs slightly better in terms of Ground Time and Time Slack, the adaptive algorithm excels overwhelmingly in terms of Change Score, offering greater flexibility in handling new maintenance information. The proposed RL-based approach can improve the efficiency of aircraft maintenance and has the potential for further research in this area. | pt |
dc.language.iso | eng | pt |
dc.publisher | Springer Nature | pt |
dc.relation | European Union’s Horizon 2020 research and innovation program under the REMAP project, grant number 769288 | pt |
dc.relation | FCT—Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit—UIDB/00326/2020 or project code UIDP/00326/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.title | Adaptive reinforcement learning for task scheduling in aircraft maintenance | pt |
dc.type | article | - |
degois.publication.firstPage | 16605 | pt |
degois.publication.issue | 1 | pt |
degois.publication.title | Scientific Reports | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1038/s41598-023-41169-3 | pt |
degois.publication.volume | 13 | pt |
dc.date.embargo | 2023-10-03 | * |
uc.date.periodoEmbargo | 0 | pt |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | article | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | Com Texto completo | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0002-5656-0061 | - |
crisitem.author.orcid | 0000-0002-7019-7721 | - |
crisitem.author.orcid | 0000-0002-9770-7672 | - |
Appears in Collections: | I&D CISUC - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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Adaptive-reinforcement-learning-for-task-scheduling-in-aircraft-maintenanceScientific-Reports.pdf | 1.77 MB | Adobe PDF | View/Open |
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