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A decision support framework for handling schedule perturbation

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  • Yan, Shangyao
  • Yang, Dah-Hwei

Abstract

Perturbations of flight schedules may occur everyday. Poor scheduling of flights may result in a substantial loss of profit and decreased levels of service for air carriers. This research aims at developing a framework to help carriers in handling schedule perturbations caused by the breakdown of aircraft. The framework is based on a basic schedule perturbation model constructed as a dynamic network from which several perturbed network models are developed for scheduling following incidents. These network models are formulated as pure network flow problems or network flow problems with side constraints. The former are solved using the network simplex method while the latter are solved using Lagrangian relaxation with subgradient methods. To show how to apply the framework in the real world, a case study focusing on the operations of a major Taiwan air carrier in East Asia is presented.

Suggested Citation

  • Yan, Shangyao & Yang, Dah-Hwei, 1996. "A decision support framework for handling schedule perturbation," Transportation Research Part B: Methodological, Elsevier, vol. 30(6), pages 405-419, December.
    • Handle: RePEc:eee:transb:v:30:y:1996:i:6:p:405-419
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    Cited by:

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    7. Huang, Zhouchun & Luo, Xiaodong & Jin, Xianfei & Karichery, Sureshan, 2022. "An iterative cost-driven copy generation approach for aircraft recovery problem," European Journal of Operational Research, Elsevier, vol. 301(1), pages 334-348.
    8. Alderighi, Marco & Gaggero, Alberto A., 2018. "Flight cancellations and airline alliances: Empirical evidence from Europe," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 90-101.
    9. Santos, Bruno F. & Wormer, Maarten M.E.C. & Achola, Thomas A.O. & Curran, Richard, 2017. "Airline delay management problem with airport capacity constraints and priority decisions," Journal of Air Transport Management, Elsevier, vol. 63(C), pages 34-44.
    10. Stephen J. Maher, 2016. "Solving the Integrated Airline Recovery Problem Using Column-and-Row Generation," Transportation Science, INFORMS, vol. 50(1), pages 216-239, February.
    11. Jay M. Rosenberger & Ellis L. Johnson & George L. Nemhauser, 2003. "Rerouting Aircraft for Airline Recovery," Transportation Science, INFORMS, vol. 37(4), pages 408-421, November.
    12. Abdelghany, Khaled F. & Abdelghany, Ahmed F. & Ekollu, Goutham, 2008. "An integrated decision support tool for airlines schedule recovery during irregular operations," European Journal of Operational Research, Elsevier, vol. 185(2), pages 825-848, March.
    13. Shangyao Yan & Yu-Lin Shih & Wang-Tsang Lee, 2011. "A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems," Journal of Global Optimization, Springer, vol. 49(4), pages 539-559, April.
    14. Zhang, Yu, 2008. "Real-time Inter-modal Strategies for Airline Schedule Perturbation Recovery and Airport Congestion Mitigation under Collaborative Decision Making (CDM)," University of California Transportation Center, Working Papers qt2k44c9tx, University of California Transportation Center.
    15. Yan, Shangyao & Tang, Ching-Hui & Fu, Tseng-Chih, 2008. "An airline scheduling model and solution algorithms under stochastic demands," European Journal of Operational Research, Elsevier, vol. 190(1), pages 22-39, October.
    16. Vaaben, Bo & Larsen, Jesper, 2015. "Mitigation of airspace congestion impact on airline networks," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 54-65.
    17. Yan, Shangyao & Tu, Yu-Ping, 2002. "A network model for airline cabin crew scheduling," European Journal of Operational Research, Elsevier, vol. 140(3), pages 531-540, August.
    18. Lin, Dung-Ying & Chang, Yu-Ting, 2018. "Ship routing and freight assignment problem for liner shipping: Application to the Northern Sea Route planning problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 47-70.
    19. Bard, Jonathan F. & Mohan, Dinesh Natarajan, 2008. "Reallocating arrival slots during a ground delay program," Transportation Research Part B: Methodological, Elsevier, vol. 42(2), pages 113-134, February.
    20. Paul M. Carlson, 2000. "Exploiting the Opportunities of Collaborative Decision Making: A Model and Efficient Solution Algorithm for Airline Use," Transportation Science, INFORMS, vol. 34(4), pages 381-393, November.
    21. Shangyao Yan & Chun-Ying Chen, 2011. "An optimization model and a solution algorithm for the many-to-many car pooling problem," Annals of Operations Research, Springer, vol. 191(1), pages 37-71, November.
    22. Stojkovic, Goran & Soumis, François & Desrosiers, Jacques & Solomon, Marius M., 2002. "An optimization model for a real-time flight scheduling problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 779-788, November.
    23. Hu, Yuzhen & Song, Yan & Zhao, Kang & Xu, Baoguang, 2016. "Integrated recovery of aircraft and passengers after airline operation disruption based on a GRASP algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 97-112.
    24. 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).
    25. Brouer, Berit D. & Dirksen, Jakob & Pisinger, David & Plum, Christian E.M. & Vaaben, Bo, 2013. "The Vessel Schedule Recovery Problem (VSRP) – A MIP model for handling disruptions in liner shipping," European Journal of Operational Research, Elsevier, vol. 224(2), pages 362-374.

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