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A Network-Based Model for the Integrated Weekly Aircraft Maintenance Routing and Fleet Assignment Problem

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  • Zhe Liang

    (Department of Industrial Engineering and Management, Peking University, Beijing, China, 100871)

  • Wanpracha Art Chaovalitwongse

    (Departments of Industrial and Systems Engineering and Radiology, University of Washington, Seattle, Washington 98195)

Abstract

Most studies in airline operations planning research are focused on the optimization problems that deal with a daily flight schedule, which is considered to be the same for every day in the week. While the weekly schedule is more realistic and practical, it increases the complexity of the optimization problems drastically. In this paper, we present a novel weekly rotation-tour network representation for the weekly aircraft maintenance routing problem (WAMRP). Based on this representation, we propose a new network-based mixed-integer linear programming (LP) formulation for the WAMRP; namely, weekly rotation-tour network model (WRTNM). The main advantage of this formulation is that the size of WRTNM only increases linearly with the size of the weekly schedule, and it provides a very tight LP relaxation. In addition, because of the tight LP relaxation, we develop a diving heuristic to solve WRTNM efficiently and effectively. To assess the performance of WRTNM, we tested the WRTNM using eight real-life test cases. The computational results show that the proposed model is very compact and scalable, and is able to find the optimal solutions to the schedule with 5,700 flights and 330 aircraft, approximately the size of the world's largest airlines fleet, within five minutes. We also propose an integrated model to solve the WAMRP with the weekly fleet assignment problem simultaneously. We tested the integrated model on nine self-constructed test cases. The computational results show that the integrated model generates near-optimal solutions to the schedules with 1,700 flights, 8 fleets with 110 aircraft, and approximately a medium-sized airline, in a reasonable time.

Suggested Citation

  • Zhe Liang & Wanpracha Art Chaovalitwongse, 2013. "A Network-Based Model for the Integrated Weekly Aircraft Maintenance Routing and Fleet Assignment Problem," Transportation Science, INFORMS, vol. 47(4), pages 493-507, November.
    • Handle: RePEc:inm:ortrsc:v:47:y:2013:i:4:p:493-507
    DOI: 10.1287/trsc.1120.0434
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    References listed on IDEAS

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    Cited by:

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    2. Sanchez, David Torres & Boyacı, Burak & Zografos, Konstantinos G., 2020. "An optimisation framework for airline fleet maintenance scheduling with tail assignment considerations," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 142-164.
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    4. Gao, Yuan & Xia, Jun & D’Ariano, Andrea & Yang, Lixing, 2022. "Weekly rolling stock planning in Chinese high-speed rail networks," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 295-322.
    5. Xiao, Fan & Guo, Siqi & Huang, Lin & Huang, Lei & Liang, Zhe, 2022. "Integrated aircraft tail assignment and cargo routing problem with through cargo consideration," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 328-351.
    6. Oliver Faust & Jochen Gönsch & Robert Klein, 2017. "Demand-Oriented Integrated Scheduling for Point-to-Point Airlines," Transportation Science, INFORMS, vol. 51(1), pages 196-213, February.
    7. Ding, Chengjin & Chen, Xinyuan & Wu, Weiwei & Wei, Wenbin & Xin, Zelin, 2023. "Game-theoretic analysis of the impact of crew overnight hotel cost on airlines’ fleet assignment and crew pairing," Journal of Air Transport Management, Elsevier, vol. 113(C).
    8. Yu Zhou & Leishan Zhou & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem," Complexity, Hindawi, vol. 2017, pages 1-14, July.
    9. De Bruecker, Philippe & Beliën, Jeroen & Van den Bergh, Jorne & Demeulemeester, Erik, 2018. "A three-stage mixed integer programming approach for optimizing the skill mix and training schedules for aircraft maintenance," European Journal of Operational Research, Elsevier, vol. 267(2), pages 439-452.
    10. 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.
    11. Gao, Yuan & Schmidt, Marie & Yang, Lixing & Gao, Ziyou, 2020. "A branch-and-price approach for trip sequence planning of high-speed train units," Omega, Elsevier, vol. 92(C).
    12. 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.
    13. Wen, Xin & Sun, Xuting & Ma, Hoi-Lam & Sun, Yige, 2022. "A column generation approach for operational flight scheduling and aircraft maintenance routing," Journal of Air Transport Management, Elsevier, vol. 105(C).
    14. Valentina Cacchiani & Juan-José Salazar-González, 2017. "Optimal Solutions to a Real-World Integrated Airline Scheduling Problem," Transportation Science, INFORMS, vol. 51(1), pages 250-268, February.
    15. Zhao, Ai & Bard, Jonathan F. & Bickel, J. Eric, 2023. "A two-stage approach to aircraft recovery under uncertainty," Journal of Air Transport Management, Elsevier, vol. 111(C).
    16. Zhu, Waiming & Hu, Xiaoxuan & Pei, Jun & Pardalos, Panos M., 2024. "Minimizing the total travel distance for the locker-based drone delivery: A branch-and-cut-based method," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).

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