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Modeling and Solving an Airline Schedule Generation Problem

Author

Listed:
  • A. Erdmann
  • A. Nolte
  • A. Noltemeier
  • R. Schrader

Abstract

Since opening a new flight connection or closing an existing flight has a great impact on the revenues of an airline, the generation of the flight schedule is one of the fundamental problems in airline planning processes. In this paper we concentrate on a special case of the problem which arises at charter companies. In contrast to airlines operating on regular schedules, the market for charter airlines is well-known and the schedule is allowed to change completely from period to period. Thus, precise adjustments to the demands of the market have a great potential for minimizing operating costs. We present a capacitated network design model and propose a combined branch-and-cut approach to solve this airline schedule generation problem. To tighten the linear relaxation bound, we add cutting planes which adjust the number of aircraft and the spill of passengers to the demand on each itinerary. For real-world problems from a large European charter airline we obtain solutions within a very few percent of optimality with running times in the order of minutes on a customary personal computer for most of the data sets. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • A. Erdmann & A. Nolte & A. Noltemeier & R. Schrader, 2001. "Modeling and Solving an Airline Schedule Generation Problem," Annals of Operations Research, Springer, vol. 107(1), pages 117-142, October.
    • Handle: RePEc:spr:annopr:v:107:y:2001:i:1:p:117-142:10.1023/a:1014998931654
    DOI: 10.1023/A:1014998931654
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    Citations

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    Cited by:

    1. João P. Pita & Cynthia Barnhart & António P. Antunes, 2013. "Integrated Flight Scheduling and Fleet Assignment Under Airport Congestion," Transportation Science, INFORMS, vol. 47(4), pages 477-492, November.
    2. I. Campbell & M. Montaz Ali & M. Silverwood, 2020. "Solving a dial-a-flight problem using composite variables," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 123-153, April.
    3. Pita, João Pedro & Antunes, António Pais & Barnhart, Cynthia & de Menezes, António Gomes, 2013. "Setting public service obligations in low-demand air transportation networks: Application to the Azores," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 35-48.
    4. Marco E. Lübbecke & Jacques Desrosiers, 2005. "Selected Topics in Column Generation," Operations Research, INFORMS, vol. 53(6), pages 1007-1023, December.
    5. Başak Kepir & Çağıl Koçyiğit & Işıl Koyuncu & Melis Beren Özer & Bahar Yetis Kara & Melih Akif Gürbüz, 2016. "Flight-Scheduling Optimization and Automation for AnadoluJet," Interfaces, INFORMS, vol. 46(4), pages 315-325, August.
    6. Sherali, Hanif D. & Bish, Ebru K. & Zhu, Xiaomei, 2006. "Airline fleet assignment concepts, models, and algorithms," European Journal of Operational Research, Elsevier, vol. 172(1), pages 1-30, July.
    7. 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.
    8. Gang Chen & Liping Jiang, 2016. "Managing customer arrivals with time windows: a case of truck arrivals at a congested container terminal," Annals of Operations Research, Springer, vol. 244(2), pages 349-365, September.
    9. Pita, João P. & Adler, Nicole & Antunes, António P., 2014. "Socially-oriented flight scheduling and fleet assignment model with an application to Norway," Transportation Research Part B: Methodological, Elsevier, vol. 61(C), pages 17-32.

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