Learning from healthcare: a perspective review on incorporating soft constraints into aviation maintenance scheduling
DOI: https://doi.org/10.3846/aviation.2026.26879Abstract
A key consideration in effective aviation maintenance scheduling is the satisfaction of maintenance personnel in relation to allocating tasks and work scheduling. Research that reflects the satisfaction of aviation maintenance staff is limited. Studies focusing on soft constraints in aviation maintenance are nearly non-existent. Soft constraints encompass flexible factors such as employee preferences, workload distribution, and work environment, which significantly impact employee satisfaction and job performance. Aircraft maintenance optimisation needs to consider both hard and soft constraints. Soft constraints have been extensively studied in healthcare and, as this perspective review argues, this can provide valuable insights for aviation maintenance scheduling management. Specifically, aviation maintenance requirements, such as task-based scheduling, necessitate the development of tailored tools to efficiently accommodate the sector’s particularities. This perspective review of aviation maintenance scheduling literature was focused on identifying gaps in the consideration of soft constraints. While there are some studies on incorporating soft constraints into an effective fatigue management system, there is a paucity of research in this area in aviation maintenance. Conversely, the reviewed literature reveals that hard constraints have received greater attention in modelling. This perspective review proposes the development of designated soft constraints to measure the satisfaction of aviation maintenance personnel.
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aviation maintenance, shift scheduling, soft constraints, staff satisfactionHow to Cite
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References
Abdennadher, S., & Schlenker, H. (1999). Nurse scheduling using constraint logic programming. In Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence (pp. 1–6), Orlando, Florida, USA. AAAI.
Abdullah, N., Ayob, M., Lam, M. C., Sabar, N. R., Kendall, G., & Razali, M. K. M. (2025). Optimization techniques for physician scheduling problem: A systematic review of recent advancements and future directions. IEEE Access, 13, 5203–5218. https://doi.org/10.1109/ACCESS.2024.3524599
Albakkoush, S., Pagone, E., & Salonitis, K. (2020). Scheduling challenges within maintenance repair and overhaul operations in the civil aviation sector. In TESConf 2020 – 9th International Conference on Through-life Engineering Services (pp. 1–8). SSRN. https://doi.org/10.2139/ssrn.3718006
Attride-Stirling, J. (2001). Thematic networks: An analytic tool for qualitative research. Qualitative Research, 1(3), 385–405. https://doi.org/10.1177/146879410100100307
Bruni, R., & Detti, P. (2014). A flexible discrete optimization approach to the physician scheduling problem. Operations Research for Health Care, 3(4), 191–199. https://doi.org/10.1016/j.orhc.2014.08.003
Civil Aviation Authority. (2003). Work Hours of Aircraft Maintenance Personnel (CAA Paper 2002/06). CAA. https://www.caa.co.uk/publication/download/12349
Dawson, D., Ian Noy, Y., Härmä, M., Åkerstedt, T., & Belenky, G. (2011). Modelling fatigue and the use of fatigue models in work settings. Accident Analysis & Prevention, 43(2), 549–564. https://doi.org/10.1016/j.aap.2009.12.030
De Bruecker, P., Van den Bergh, J., Belien, J., & Demeulemeester, E. (2014). A Tabu search heuristic for building aircraft maintenance personnel rosters. SSRN. https://doi.org/10.2139/ssrn.2464033
Deng, Q., Santos, B. F., & Verhagen, W. J. C. (2021). A novel decision support system for optimizing aircraft maintenance check schedule and task allocation. Decision Support Systems, 146, Article 113545. https://doi.org/10.1016/j.dss.2021.113545
Grant, T. J. (1986). Maintenance engineering management applications of artificial intelligence. In D. Sriram & R. Adey, Applications of artificial intelligence in engineering problems (pp. 1097–1121). Springer. https://doi.org/10.1007/978-3-662-21626-2_88
Hobbs, A., Bedell Avers, K., & Hiles, J. J. (2011). Fatigue risk management in aviation maintenance: Current best practices and potential future countermeasures (OK-11-0024-JAH). Semantic Scholar. https://doi.org/10.1037/e621172011-001
International Air Transport Association. (2024). IATA Annual Safety Report – 2024. https://www.iata.org/en/publications/safety-report/executive-summary/
Kil, Y., Graham, M., & Chatzi, A. V. (2024). Examination of personality types as predictors of safety attitudes/behaviours, in support of enhancing safety in healthcare: A scoping review. International Journal of Health Governance, 29(4), 323–341. https://doi.org/10.1108/IJHG-06-2024-0075
Laesanklang, W., Landa-Silva, D., & Castillo-Salazar, J. A. (2015). Mixed integer programming with decomposition to solve a workforce scheduling and routing problem. In Proceedings of the International Conference on Opeartions Research and Enterprise Systems (ICORES-2015) (pp. 283–293). Scitepress. https://doi.org/10.5220/0005223602830293
Memarzadeh, K., Kazemipoor, H., Fallah, M., & Moghaddam, B.-F. (2024). A two-stage scenario-based robust optimization model and a column-row generation method for integrated aircraft maintenance-routing and crew rostering. Computer Modeling in Engineering & Sciences, 141(2), 1275–1304. https://doi.org/10.32604/cmes.2024.050306
Niu, B., Xue, B., Zhong, H., Qiu, H., & Tianwei, Z. (2023). Short-term aviation maintenance technician scheduling based on dynamic task disassembly mechanism. Information Sciences, 629(2), 816–835. https://doi.org/10.1016/j.ins.2023.01.137
Obadimu, S. O., Karanikas, N., & Kourousis, K. I. (2020). Development of the minimum equipment list: Current practice and the need for standardisation. Aerospace, 7(1), Article 7. https://doi.org/10.3390/aerospace7010007
Peters, V. P. J., de Rijk, A. E., & Boumans, N. P. G. (2009). Nurses’ satisfaction with shiftwork and associations with work, home and health characteristics: A survey in the Netherlands. Journal of Advanced Nursing, 65(12), 2689–2700. https://doi.org/10.1111/j.1365-2648.2009.05123.x
Pimapunsri, K., Weeranant, D., & Riel, A. (2021). Genetic algorithms for the resource-constrained project scheduling problem in aircraft heavy maintenance. Suranaree Journal of Science and Technology, 28(6), Article 010083. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130588829&partnerID=40&md5=ae5da44f7f4a38c8a48062079343dc42
Rahimian, E., Akartunalı, K., & Levine, J. (2017). A hybrid integer and constraint programming approach to solve nurse rostering problems. Computers & Operations Research, 82, 83–94. https://doi.org/10.1016/j.cor.2017.01.016
Ryan, T. (2022). Facilitators of person and relationship-centred care in nursing. Nursing Open, 9(2), 892–899. https://doi.org/10.1002/nop2.1083
Sachon, M., & Paté-Cornell, E. (2000). Delays and safety in airline maintenance. Reliability Engineering & System Safety, 67(3), 301–309. https://doi.org/10.1016/S0951-8320(99)00062-9
Santos, L. F. F. M., & Melicio, R. (2019). Stress, pressure and fatigue on aircraft maintenance personal. International Review of Aerospace Engineering (IREASE), 12(1). https://doi.org/10.15866/irease.v12i1.14860
Shaukat, S. (2015). Aircraft line maintenance scheduling optimisation-a heuristic approach [Masters Thesis, UNSW Sydney]. UNSW Sydney Library.
Shaukat, S., Katscher, M., Wu, C.-L., Delgado, F., & Larrain, H. (2020). Aircraft line maintenance scheduling and optimisation. Journal of Air Transport Management, 89, Article 101914. https://doi.org/10.1016/j.jairtraman.2020.101914
Solos, I. P., Tassopoulos, I. X., & Beligiannis, G. N. (2013). A generic two-phase stochastic variable neighborhood approach for effectively solving the nurse rostering problem. Algorithms, 6(2), 278–308. https://doi.org/10.3390/a6020278
Sriram, C., & Haghani, A. (2003). An optimization model for aircraft maintenance scheduling and re-assignment. Transportation Research Part A: Policy and Practice, 37(1), 29–48. https://doi.org/10.1016/S0965-8564(02)00004-6
Uhde, A., Schlicker, N., Wallach, D. P., & Hassenzahl, M. (2020). Fairness and decision-making in collaborative shift scheduling systems. In CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–13), Honolulu, HI, USA. ACM Digital Library. https://doi.org/10.1145/3313831.3376656
Vianna, W. O. L., & Yoneyama, T. (2018). Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles. IEEE Systems Journal, 12(2), 1170–1181. https://doi.org/10.1109/JSYST.2017.2667232
Wang, T.-C., & Liu, C.-C. (2014). Optimal work shift scheduling with fatigue minimization and day off preferences. Mathematical Problems in Engineering, 2014(1), Article 751563. https://doi.org/10.1155/2014/751563
Weide, T. M. J. van der, Deng, Q., & Santos, B. F. (2022). Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty. Computers & Operations Research, 141, Article 105667. https://doi.org/10.1016/j.cor.2021.105667
Xue, B., Qiu, H., Niu, B., & Yan, X. (2022). Improved aircraft maintenance technician scheduling with task splitting strategy based on particle swarm optimization. In Y. Tan, Y. Shi, & B. Niu (Eds), Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science (Vol. 13344). Springer. https://doi.org/10.1007/978-3-031-09677-8_18
Yan, S., Yang, T.-H., & Chen, H.-H. (2004). Airline short-term maintenance manpower supply planning. Transportation Research Part A: Policy and Practice, 38(9–10), 615–642. https://doi.org/10.1016/j.tra.2004.03.005
Yildiz, B. C., Gzara, F., & Elhedhli, S. (2017). Airline crew pairing with fatigue: Modeling and analysis. Transportation Research Part C: Emerging Technologies, 74, 99–112. https://doi.org/10.1016/j.trc.2016.11.002
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