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Aircraft replacement scheduling: A dynamic programming approach

Author

Listed:
  • Hsu, Chaug-Ing
  • Li, Hui-Chieh
  • Liu, Su-Miao
  • Chao, Ching-Cheng

Abstract

This study developed a stochastic dynamic programming model to optimize airline decisions regarding purchasing, leasing, or disposing of aircraft over time. Grey topological models with Markov-chain were employed to forecast passenger traffic and capture the randomness of the demand. The results show that severe demand fluctuations would drive the airline to lease rather than to purchase its aircrafts. This would allow greater flexibility in fleet management and allows for matching short-term variations in the demand. The results of this study provide a useful reference for airlines in their replacement decision-making procedure by taking into consideration the fluctuations in the market demand and the status of the aircraft.

Suggested Citation

  • Hsu, Chaug-Ing & Li, Hui-Chieh & Liu, Su-Miao & Chao, Ching-Cheng, 2011. "Aircraft replacement scheduling: A dynamic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(1), pages 41-60, January.
    • Handle: RePEc:eee:transe:v:47:y:2011:i:1:p:41-60
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    Citations

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    Cited by:

    1. Lay Eng Teoh & Hooi Ling Khoo, 2016. "Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase," Journal of Optimization, Hindawi, vol. 2016, pages 1-12, June.
    2. Sa, Constantijn A.A. & Santos, Bruno F. & Clarke, John-Paul B., 2020. "Portfolio-based airline fleet planning under stochastic demand," Omega, Elsevier, vol. 97(C).
    3. Geursen, Izaak L. & Santos, Bruno F. & Yorke-Smith, Neil, 2023. "Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning," Journal of Air Transport Management, Elsevier, vol. 109(C).
    4. Rosskopf, Michael & Lehner, Stephan & Gollnick, Volker, 2014. "Economic–environmental trade-offs in long-term airline fleet planning," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 109-115.
    5. Birolini, Sebastian & Jacquillat, Alexandre & Cattaneo, Mattia & Antunes, António Pais, 2021. "Airline Network Planning: Mixed-integer non-convex optimization with demand–supply interactions," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 100-124.
    6. Chen, Wei-Ting & Wu, Cheng-Lung, 2023. "Aircraft acquisition optimization under demand and cost fluctuations: Before and after leasing standard changes," Journal of Air Transport Management, Elsevier, vol. 112(C).
    7. Jing Zhou, 2023. "Airline capacity distribution under financial budget and resource consideration," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-29, July.
    8. Chen, Wei-Ting & Huang, Kuancheng & Ardiansyah, Muhammad Nashir, 2018. "A mathematical programming model for aircraft leasing decisions," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 15-25.
    9. Carreira, Joana S. & Lulli, Guglielmo & Antunes, António P., 2017. "The airline long-haul fleet planning problem: The case of TAP service to/from Brazil," European Journal of Operational Research, Elsevier, vol. 263(2), pages 639-651.
    10. Zhen, Lu & Wang, Shuaian & Zhuge, Dan, 2017. "Dynamic programming for optimal ship refueling decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 63-74.
    11. Alavi Fard, Farzad & Sy, Malick & Ivanov, Dmitry, 2019. "Optimal overbooking strategies in the airlines using dynamic programming approach in continuous time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 384-399.
    12. Bazargan, Massoud & Hartman, Joseph, 2012. "Aircraft replacement strategy: Model and analysis," Journal of Air Transport Management, Elsevier, vol. 25(C), pages 26-29.
    13. Winkelmann, Jonas & Spinler, Stefan & Neukirchen, Thomas, 2024. "Green transport fleet renewal using approximate dynamic programming: A case study in German heavy-duty road transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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