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Airline Crew Scheduling Under Uncertainty

Author

Listed:
  • Andrew J. Schaefer

    (Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261)

  • Ellis L. Johnson

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Anton J. Kleywegt

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • George L. Nemhauser

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

Airline crew scheduling algorithms widely used in practice assume no disruptions. Because disruptions often occur, the actual cost of the resulting crew schedules is often greater. We consider algorithms for finding crew schedules that perform well in practice. The deterministic crew scheduling model is an approximation of crew scheduling under uncertainty with the assumption that all pairings will operate as planned. We seek better approximate solution methods for crew scheduling under uncertainty that still remain tractable. We give computational results from three fleets that indicate that the crew schedules obtained from our method perform better in a model of operations with disruptions than the crew schedules found via deterministic methods. Under mild assumptions we provide a lower bound on the cost of an optimal crew schedule in operations, and we demonstrate that some of the crew schedules found using our method perform very well relative to this lower bound.

Suggested Citation

  • Andrew J. Schaefer & Ellis L. Johnson & Anton J. Kleywegt & George L. Nemhauser, 2005. "Airline Crew Scheduling Under Uncertainty," Transportation Science, INFORMS, vol. 39(3), pages 340-348, August.
    • Handle: RePEc:inm:ortrsc:v:39:y:2005:i:3:p:340-348
    DOI: 10.1287/trsc.1040.0091
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    References listed on IDEAS

    as
    1. Karla L. Hoffman & Manfred Padberg, 1993. "Solving Airline Crew Scheduling Problems by Branch-and-Cut," Management Science, INFORMS, vol. 39(6), pages 657-682, June.
    2. Jay M. Rosenberger & Andrew J. Schaefer & David Goldsman & Ellis L. Johnson & Anton J. Kleywegt & George L. Nemhauser, 2002. "A Stochastic Model of Airline Operations," Transportation Science, INFORMS, vol. 36(4), pages 357-377, November.
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