Determining aircraft maintenance times in civil aviation under the learning effect

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dc.authorscopusid54951017400
dc.authorscopusid35749312800
dc.contributor.authorAtici, Uğur
dc.contributor.authorŞenol, M.B.
dc.date.accessioned2023-07-26T11:59:03Z
dc.date.available2023-07-26T11:59:03Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractPurpose: Scheduling of aircraft maintenance operations is a gap in the literature. Maintenance times should be determined close to the real-life to schedule aircraft maintenance operations effectively. The learning effect, which has been studied extensively in the machine scheduling literature, has not been investigated on aircraft maintenance times. In the literature, the production times under the learning effect have been examined in numerous studies but for merely manufacturing and assembly lines. A model for determining base and line maintenance times in civil aviation under the learning effect has not been proposed yet. It is pretty challenging to determine aircraft maintenance times due to the various aircraft configurations, extended maintenance periods, different worker shifts and workers with diverse experience and education levels. The purpose of this study is to determine accurate aircraft maintenance times rigorously with a new model which includes the group learning effect with the multi-products and shifts, plateau effect, multi sub-operations and labour firings/rotations. Design/methodology/approach: Aircraft maintenance operations are carried out in shifts. Each maintenance operation consists of many sub-operations that are performed by groups of workers. Thus, various models, e.g. learning curve for maintenance line (MLC), MLC with plateau factor (MPLC), MLC with group factor (MGLC) were developed and used in this study. The performance and efficiency of the models were compared with the current models in the literature, such as the Yelle Learning model (Yelle), single learning curve (SLC) model and SLC with plateau factor model (SLC-P). Estimations of all these models were compared with actual aircraft maintenance times in terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square of the error (MSE) values. Seven years (2014–2020) maintenance data of one of the top ten maintenance companies in civil aviation were analysed for the application and comparison of learning curve models. Findings: The best estimations in terms of MAD, MAPE and MSE values are, respectively, gathered by MGLC, SLC-P, MPLC, MLC, SLC and YELLE models. This study revealed that the models (MGLC, SLC-P, MPLC), including the plateau factor, are more efficient in estimating accurate aircraft maintenance times. Furthermore, MGLC always made the closest estimations to the actual aircraft maintenance times. The results show that the MGLC model is more accurate than all of the other models for all sub-operations. The MGLC model is promising for the aviation industry in determining aircraft maintenance times under the learning effect. Originality/value: In this study, learning curve models, considering groups of workers working in shifts, have been developed and employed for the first time for estimating more realistic maintenance times in aircraft maintenance. To the best of the authors’ knowledge, the effect of group learning on maintenance times in aircraft maintenance operations has not been studied. The novelty of the models are their applicability for groups of workers with different education and experience levels working in the same shift where they can learn in accordance with their proportion of contribution to the work and learning continues throughout shifts. The validity of the proposed models has been proved by comparing actual aircraft maintenance data. In practice, the MGLC model could efficiently be used for aircraft maintenance planning, certifying staff performance evaluations and maintenance trainings. Moreover, aircraft maintenance activities can be scheduled under the learning effect and a more realistic maintenance plan could be gathered in that way. © 2022, Emerald Publishing Limited.en_US
dc.description.sponsorshipNo support was received from any organization during the preparation of this study.en_US
dc.identifier.doi10.1108/AEAT-05-2021-0153
dc.identifier.endpage1378en_US
dc.identifier.issn1748-8842
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85127162252en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1366en_US
dc.identifier.urihttps://doi.org/10.1108/AEAT-05-2021-0153
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13625
dc.identifier.volume94en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞenol, M.B
dc.language.isoenen_US
dc.publisherEmerald Group Holdings Ltd.en_US
dc.relation.ispartofAircraft Engineering and Aerospace Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectAircraft maintenanceen_US
dc.subjectAircraft maintenance planningen_US
dc.subjectLearning curveen_US
dc.subjectMaintenance timesen_US
dc.subjectOptimizationen_US
dc.subjectProductivityen_US
dc.subjectAircraften_US
dc.subjectCivil aviationen_US
dc.subjectCurve fittingen_US
dc.subjectEfficiencyen_US
dc.subjectEstimationen_US
dc.subjectLearning systemsen_US
dc.subjectManufactureen_US
dc.subjectProductivityen_US
dc.subjectSchedulingen_US
dc.subjectAircraft maintenanceen_US
dc.subjectAircraft maintenance planningen_US
dc.subjectLearning curvesen_US
dc.subjectLearning effectsen_US
dc.subjectMaintenance operationsen_US
dc.subjectMaintenance planningen_US
dc.subjectMaintenance timeen_US
dc.subjectOptimisationsen_US
dc.subjectWorkers'en_US
dc.subjectMaintenanceen_US
dc.titleDetermining aircraft maintenance times in civil aviation under the learning effecten_US
dc.typeArticleen_US

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