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A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization

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  • Deng, Qichen
  • Santos, Bruno F.
  • Curran, Richard

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.

Suggested Citation

  • Deng, Qichen & Santos, Bruno F. & Curran, Richard, 2020. "A practical dynamic programming based methodology for aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 281(2), pages 256-273.
  • Handle: RePEc:eee:ejores:v:281:y:2020:i:2:p:256-273
    DOI: 10.1016/j.ejor.2019.08.025
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    Citations

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    Cited by:

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    2. Deng, Qichen & Santos, Bruno F., 2022. "Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization," European Journal of Operational Research, Elsevier, vol. 299(3), pages 814-833.
    3. Witteman, Max & Deng, Qichen & Santos, Bruno F., 2021. "A bin packing approach to solve the aircraft maintenance task allocation problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 365-376.
    4. Tseremoglou, Iordanis & Santos, Bruno F., 2024. "Condition-Based Maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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    6. Cha, Guesik & Park, Junseok & Moon, Ilkyeong, 2023. "Military aircraft flight and maintenance planning model considering heterogeneous maintenance tasks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
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    8. Changjiu Li & Yong Zhang & Xichao Su & Xinwei Wang, 2022. "An Improved Optimization Algorithm for Aeronautical Maintenance and Repair Task Scheduling Problem," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    9. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    10. Qin, Yichen & Ng, Kam K.H., 2023. "Analysing the impact of collaborations between airlines and maintenance service company under MRO outsourcing mode: Perspective from airline's operations," Journal of Air Transport Management, Elsevier, vol. 109(C).
    11. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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    14. Radim Briš & Nuong Thi Thuy Tran, 2023. "Discrete Model for a Multi-Objective Maintenance Optimization Problem of Safety Systems," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

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