Abstract
This paper presents a practical dynamic programming based methodology to optimize the long-term maintenance check schedule for a fleet of heterogeneous aircraft. It is the first time that the long-term aircraft maintenance check schedule is optimized, integrating different check types in a single schedule solution. The proposed methodology aims at minimizing the wasted interval between checks. By achieving this goal, one is also reducing the number of checks over time, increasing aircraft availability and, therefore, reducing maintenance costs, while respecting safety regulations. The model formulation takes aircraft type, status, maintenance capacity, and other operational constraints into consideration. We also validate and demonstrate the proposed methodology using fleet maintenance data from a European airline. The outcomes show that, when compared with the current practice, the number of maintenance checks can be reduced by around 7% over a period of 4 years, while computation time is less than 15 minutes. This could result in saving worth $1.1M–$3.4M in maintenance costs for a fleet of about 40 aircraft and generating more than $9.8M of revenue due to higher aircraft availability.
Original language | English |
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Pages (from-to) | 256-273 |
Number of pages | 18 |
Journal | European Journal of Operational Research |
Volume | 281 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2020 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Aircraft maintenance
- Dynamic programming
- Forward induction
- Scheduling
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Aircraft Maintenance Check Scheduling Data Set
Deng, Q. (Creator), Santos, B. F. (Creator) & Curran, R. (Creator), TU Delft - 4TU.ResearchData, 8 Aug 2019
DOI: 10.4121/UUID:1630E6FD-9574-46E8-899E-83037C17BCEF
Dataset/Software: Dataset