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Tackling uncertainties in aircraft maintenance routing: A review of emerging technologies

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  • Ma, Hoi-Lam
  • Sun, Yige
  • Chung, Sai-Ho
  • Chan, Hing Kai

Abstract

Aircraft maintenance routing (AMR) is crucial for both passenger airlines and cargo airlines to maintain flight operations efficiency, meanwhile it ensures that aircraft fulfill the civil aviation safety regulations in respect to maintenance checking. An optimal AMR solution can maximize the aircraft utilization for significantly increasing the airlines’ transport capacity and profitability. Therefore, extensive research works have been undertaken in this area. Traditionally, AMRs are optimized based on deterministic approaches. However, the theoretical optimality of the solution is usually difficult to achieve during practical operations due to disruptions arising from uncertainties. Thus, optimization of AMRs with uncertainty considerations has become more prevalent. More importantly, because of the maturity of technologies such as sensors nowadays, applications of these emerging technologies to tackle uncertainties have attracted much attention. Motivated by this, we conducted a critical review on AMR papers in handling uncertainties with emerging technologies. Currently, this is the first review in AMRs that contributes by systematically reviewing the emerging technologies in handling uncertainties in AMR. By reviewing 75 papers in the area, we have categorized the papers into four areas: uncertainty and robust solutions, big data and machine learning, smart technologies, and integrated information support systems. Based on the above, we also suggest some future research directions for researchers in the area.

Suggested Citation

  • Ma, Hoi-Lam & Sun, Yige & Chung, Sai-Ho & Chan, Hing Kai, 2022. "Tackling uncertainties in aircraft maintenance routing: A review of emerging technologies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:transe:v:164:y:2022:i:c:s1366554522001946
    DOI: 10.1016/j.tre.2022.102805
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    1. Xiangsheng Dou, 2020. "Big data and smart aviation information management system," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1766736-176, January.
    2. Kenan, Nabil & Jebali, Aida & Diabat, Ali, 2018. "The integrated aircraft routing problem with optional flights and delay considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 355-375.
    3. Chiwei Yan & Jerry Kung, 2018. "Robust Aircraft Routing," Transportation Science, INFORMS, vol. 52(1), pages 118-133, January.
    4. Wang, Yuhao & Pang, Yutian & Chen, Oliver & Iyer, Hari N. & Dutta, Parikshit & Menon, P.K. & Liu, Yongming, 2021. "Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    5. Kammoun, Mohamed Ali & Rezg, Nidhal, 2018. "An efficient hybrid approach for resolving the aircraft routing and rescheduling problem," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 73-87.
    6. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    7. Wu, Cheng-Lung & Law, Kristie, 2019. "Modelling the delay propagation effects of multiple resource connections in an airline network using a Bayesian network model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 62-77.
    8. Khan, Waqar Ahmed & Ma, Hoi-Lam & Ouyang, Xu & Mo, Daniel Y., 2021. "Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    9. Bo Zhu & Jin-fu Zhu & Qiang Gao, 2015. "A Stochastic Programming Approach on Aircraft Recovery Problem," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, October.
    10. Bombelli, Alessandro & Fazi, Stefano, 2022. "The ground handler dock capacitated pickup and delivery problem with time windows: A collaborative framework for air cargo operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    11. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    12. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
    13. Jamili, Amin, 2017. "A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem," Journal of Air Transport Management, Elsevier, vol. 58(C), pages 21-30.
    14. Ben Ahmed, Mohamed & Zeghal Mansour, Farah & Haouari, Mohamed, 2018. "Robust integrated maintenance aircraft routing and crew pairing," Journal of Air Transport Management, Elsevier, vol. 73(C), pages 15-31.
    15. Tseremoglou, Iordanis & Bombelli, Alessandro & Santos, Bruno F., 2022. "A combined forecasting and packing model for air cargo loading: A risk-averse framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    16. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2019. "A supporting framework for maintenance capacity planning and scheduling: Development and application in the aircraft MRO industry," International Journal of Production Economics, Elsevier, vol. 218(C), pages 1-15.
    17. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    18. Başdere, Mehmet & Bilge, Ümit, 2014. "Operational aircraft maintenance routing problem with remaining time consideration," European Journal of Operational Research, Elsevier, vol. 235(1), pages 315-328.
    19. Jane Lee & Lavanya Marla & Alexandre Jacquillat, 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty," Transportation Science, INFORMS, vol. 54(4), pages 973-997, July.
    20. Maher, Stephen J. & Desaulniers, Guy & Soumis, François, 2018. "The daily tail assignment problem under operational uncertainty using look-ahead maintenance constraints," European Journal of Operational Research, Elsevier, vol. 264(2), pages 534-547.
    21. Hanif Sherali & Ki-Hwan Bae & Mohamed Haouari, 2013. "A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture," Annals of Operations Research, Springer, vol. 210(1), pages 213-244, November.
    22. 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.
    23. 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.
    24. Carlos Lagos & Felipe Delgado & Mathias A. Klapp, 2020. "Dynamic Optimization for Airline Maintenance Operations," Transportation Science, INFORMS, vol. 54(4), pages 998-1015, July.
    25. Xu, Xiaoping & Choi, Tsan-Ming, 2021. "Supply chain operations with online platforms under the cap-and-trade regulation: Impacts of using blockchain technology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    26. Xu, Yifan & Wandelt, Sebastian & Sun, Xiaoqian, 2021. "Airline integrated robust scheduling with a variable neighborhood search based heuristic," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 181-203.
    27. Sai Ho Chung & Hoi Lam Ma & Hing Kai Chan, 2017. "Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1443-1458, August.
    28. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "A model enhancement heuristic for building robust aircraft maintenance personnel rosters with stochastic constraints," European Journal of Operational Research, Elsevier, vol. 246(2), pages 661-673.
    29. Sebastian Ruther & Natashia Boland & Faramroze G. Engineer & Ian Evans, 2017. "Integrated Aircraft Routing, Crew Pairing, and Tail Assignment: Branch-and-Price with Many Pricing Problems," Transportation Science, INFORMS, vol. 51(1), pages 177-195, February.
    30. Eufrásio, Ana Beatriz R. & Eller, Rogéria A.G. & Oliveira, Alessandro V.M., 2021. "Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    31. Choi, Tsan-Ming & Luo, Suyuan, 2019. "Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 139-152.
    32. Evler, Jan & Asadi, Ehsan & Preis, Henning & Fricke, Hartmut, 2021. "Airline ground operations: Optimal schedule recovery with uncertain arrival times," Journal of Air Transport Management, Elsevier, vol. 92(C).
    33. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    34. Guo, Zhen & Hao, Mengyan & Yu, Bin & Yao, Baozhen, 2022. "Detecting delay propagation in regional air transport systems using convergent cross mapping and complex network theory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
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