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Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem

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  • Sinclair, Karine
  • Cordeau, Jean-François
  • Laporte, Gilbert

Abstract

Because most commercial passenger airlines operate on a hub-and-spoke network, small disturbances can cause major disruptions in their planned schedules and have a significant impact on their operational costs and performance. When a disturbance occurs, the airline often applies a recovery policy in order to quickly resume normal operations. We present in this paper a large neighborhood search heuristic to solve an integrated aircraft and passenger recovery problem. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during the recovery period. The method is based on an existing heuristic, developed in the context of the 2009 ROADEF Challenge, which alternates between three phases: construction, repair and improvement. We introduce a number of refinements in each phase so as to perform a more thorough search of the solution space. The resulting heuristic performs very well on the instances introduced for the challenge, obtaining the best known solution for 17 out of 22 instances within five minutes of computing time and 21 out of 22 instances within 10minutes of computing time.

Suggested Citation

  • Sinclair, Karine & Cordeau, Jean-François & Laporte, Gilbert, 2014. "Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem," European Journal of Operational Research, Elsevier, vol. 233(1), pages 234-245.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:1:p:234-245
    DOI: 10.1016/j.ejor.2013.08.034
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    2. 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.
    3. Zhang, Dong & Yu, Chuhang & Desai, Jitamitra & Lau, H.Y.K. Henry, 2016. "A math-heuristic algorithm for the integrated air service recovery," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 211-236.
    4. Derui Wang & Yanfeng Wu & Jian-Qiang Hu & Miaomiao Liu & Peiwen Yu & Cheng Zhang & Yan Wu, 2019. "Flight Schedule Recovery: A Simulation-Based Approach," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-19, December.
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    6. Huang, Lei & Xiao, Fan & Zhou, Jing & Duan, Zhenya & Zhang, Hua & Liang, Zhe, 2023. "A machine learning based column-and-row generation approach for integrated air cargo recovery problem," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    7. Nianyi Wang & Huiling Wang & Shan Pei & Boyu Zhang, 2023. "A Data-Driven Heuristic Method for Irregular Flight Recovery," Mathematics, MDPI, vol. 11(11), pages 1-22, June.
    8. 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.
    9. Ng, K.K.H. & Lee, C.K.M. & Chan, Felix T.S. & Qin, Yichen, 2017. "Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 115-136.
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