Aircraft Maintenance Check Scheduling Using Reinforcement Learning

This 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 sch...

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Main Authors:	Pedro Andrade, Catarina Silva, Bernardete Ribeiro, Bruno F. Santos
Format:	Article
Language:	English
Published:	MDPI AG 2021-04-01
Series:	Aerospace
Subjects:	aircraft maintenance maintenance check scheduling reinforcement learning q-learning
Online Access:	https://www.mdpi.com/2226-4310/8/4/113

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spelling	doaj-9a2fd8521df24e9e97b94adb5cd4e5e82021-04-17T23:00:06ZengMDPI AGAerospace2226-43102021-04-01811311310.3390/aerospace8040113Aircraft Maintenance Check Scheduling Using Reinforcement LearningPedro Andrade0Catarina Silva1Bernardete Ribeiro2Bruno F. Santos3Department of Informatics Engineering, University of Coimbra, CISUC, 3030-290 Coimbra, PortugalDepartment of Informatics Engineering, University of Coimbra, CISUC, 3030-290 Coimbra, PortugalDepartment of Informatics Engineering, University of Coimbra, CISUC, 3030-290 Coimbra, PortugalAir Transport and Operations, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The NetherlandsThis 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.https://www.mdpi.com/2226-4310/8/4/113aircraft maintenancemaintenance check schedulingreinforcement learningq-learning
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Pedro Andrade Catarina Silva Bernardete Ribeiro Bruno F. Santos
spellingShingle	Pedro Andrade Catarina Silva Bernardete Ribeiro Bruno F. Santos Aircraft Maintenance Check Scheduling Using Reinforcement Learning Aerospace aircraft maintenance maintenance check scheduling reinforcement learning q-learning
author_facet	Pedro Andrade Catarina Silva Bernardete Ribeiro Bruno F. Santos
author_sort	Pedro Andrade
title	Aircraft Maintenance Check Scheduling Using Reinforcement Learning
title_short	Aircraft Maintenance Check Scheduling Using Reinforcement Learning
title_full	Aircraft Maintenance Check Scheduling Using Reinforcement Learning
title_fullStr	Aircraft Maintenance Check Scheduling Using Reinforcement Learning
title_full_unstemmed	Aircraft Maintenance Check Scheduling Using Reinforcement Learning
title_sort	aircraft maintenance check scheduling using reinforcement learning
publisher	MDPI AG
series	Aerospace
issn	2226-4310
publishDate	2021-04-01
description	This 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.
topic	aircraft maintenance maintenance check scheduling reinforcement learning q-learning
url	https://www.mdpi.com/2226-4310/8/4/113
work_keys_str_mv	AT pedroandrade aircraftmaintenancecheckschedulingusingreinforcementlearning AT catarinasilva aircraftmaintenancecheckschedulingusingreinforcementlearning AT bernardeteribeiro aircraftmaintenancecheckschedulingusingreinforcementlearning AT brunofsantos aircraftmaintenancecheckschedulingusingreinforcementlearning
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Aircraft Maintenance Check Scheduling Using Reinforcement Learning

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