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Unified tail assignment and maintenance task scheduling: A decision support framework for improved efficiency and stability

Author

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  • Pescio, Luigi
  • Ribeiro, Marta
  • Santos, Bruno F.

Abstract

Flight and maintenance scheduling pose conflicting objectives: while maintenance is vital for ensuring aircraft airworthiness, it comes at the cost of taking aircraft out of operation. In current operations, airlines manually handle tail assignment and maintenance task scheduling separately, missing an opportunity to strike a better balance. This division leads to wasted maintenance resources, restricted fleet availability for schedule flexibility, inconsistent planning, and neglect of schedule resilience. This study presents a novel approach that integrates tail assignment and maintenance scheduling into a unified decision-support framework. An integer program, tailored to meet airline-specific requirements and constraints, is combined with an innovative time-space network (TSN). The TSN incorporates two distinct spaces for maintenance and network activities. The primary objective is to generate feasible plans that increase schedule efficiency (i.e., no cancellations, high fleet availability, high fleet health, and optimal use of maintenance resources) and schedule stability (i.e., limited number of late arrival disruptions during operations) the day before operation. Additionally, this framework addresses overlooked aspects in the literature: it treats maintenance tasks as variable interval activities based on aircraft-specific needs, departing from the traditional fixed interval approach. The performance of the framework is tested with real-data provided by a major European single hub-to-spoke airline, with a heterogeneous fleet of over 50 wide-body aircraft. Historical data from arrival delays is used to create robust buffers that mitigate delay propagation. A 17% reduction in maintenance time was achieved compared to the airline’s current plans, resulting in a 10% increase in fleet availability on the day of operations. This improvement is attributed to higher labour and task interval utilization, indicating the framework’s superior efficiency in scheduling maintenance tasks. Lastly, the framework produced plans more resilient to arrival delays, reducing the number of disruptions and delay propagation over 40%. This framework can be used as a decision-support tool for airlines, enabling the creation of schedules that are both robust against delays and optimized for fleet utilization.

Suggested Citation

  • Pescio, Luigi & Ribeiro, Marta & Santos, Bruno F., 2025. "Unified tail assignment and maintenance task scheduling: A decision support framework for improved efficiency and stability," Operations Research Perspectives, Elsevier, vol. 15(C).
    • Handle: RePEc:eee:oprepe:v:15:y:2025:i:c:s2214716025000399
    DOI: 10.1016/j.orp.2025.100363
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Shan Lan & John-Paul Clarke & Cynthia Barnhart, 2006. "Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions," Transportation Science, INFORMS, vol. 40(1), pages 15-28, February.
    4. 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.
    5. Liang, Zhe & Feng, Yuan & Zhang, Xiaoning & Wu, Tao & Chaovalitwongse, Wanpracha Art, 2015. "Robust weekly aircraft maintenance routing problem and the extension to the tail assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 238-259.
    6. Liang, Zhe & Xiao, Fan & Qian, Xiongwen & Zhou, Lei & Jin, Xianfei & Lu, Xuehua & Karichery, Sureshan, 2018. "A column generation-based heuristic for aircraft recovery problem with airport capacity constraints and maintenance flexibility," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 70-90.
    7. Carlos Lagos & Felipe Delgado & Mathias A. Klapp, 2020. "Dynamic Optimization for Airline Maintenance Operations," Transportation Science, INFORMS, vol. 54(4), pages 998-1015, July.
    8. Shervin AhmadBeygi & Amy Cohn & Marcial Lapp, 2010. "Decreasing airline delay propagation by re-allocating scheduled slack," IISE Transactions, Taylor & Francis Journals, vol. 42(7), pages 478-489.
    9. Jeph Abara, 1989. "Applying Integer Linear Programming to the Fleet Assignment Problem," Interfaces, INFORMS, vol. 19(4), pages 20-28, August.
    10. Shaukat, Syed & Katscher, Mathias & Wu, Cheng-Lung & Delgado, Felipe & Larrain, Homero, 2020. "Aircraft line maintenance scheduling and optimisation," Journal of Air Transport Management, Elsevier, vol. 89(C).
    11. Barry C. Smith & Ellis L. Johnson, 2006. "Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition," Transportation Science, INFORMS, vol. 40(4), pages 497-516, November.
    12. Cynthia Barnhart & Timothy S. Kniker & Manoj Lohatepanont, 2002. "Itinerary-Based Airline Fleet Assignment," Transportation Science, INFORMS, vol. 36(2), pages 199-217, May.
    13. Cynthia Barnhart & Natashia L. Boland & Lloyd W. Clarke & Ellis L. Johnson & George L. Nemhauser & Rajesh G. Shenoi, 1998. "Flight String Models for Aircraft Fleeting and Routing," Transportation Science, INFORMS, vol. 32(3), pages 208-220, August.
    14. van Kessel, Paul J. & Freeman, Floris C. & Santos, Bruno F., 2023. "Airline maintenance task rescheduling in a disruptive environment," European Journal of Operational Research, Elsevier, vol. 308(2), pages 605-621.
    15. Sriram, Chellappan & Haghani, Ali, 2003. "An optimization model for aircraft maintenance scheduling and re-assignment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(1), pages 29-48, January.
    16. Gary Froyland & Stephen J. Maher & Cheng-Lung Wu, 2014. "The Recoverable Robust Tail Assignment Problem," Transportation Science, INFORMS, vol. 48(3), pages 351-372, August.
    17. Jay M. Rosenberger & Ellis L. Johnson & George L. Nemhauser, 2004. "A Robust Fleet-Assignment Model with Hub Isolation and Short Cycles," Transportation Science, INFORMS, vol. 38(3), pages 357-368, August.
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