The Recoverable Robust Tail Assignment Problem

Schedule disruptions are commonplace in the airline industry with many flight-delaying events occurring each day. Recently there has been a focus on introducing robustness into airline planning stages to reduce the effect of these disruptions. The authors propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. The authors formulate the recoverable robust tail assignment problem (RRTAP) as a stochastic program, solved using column generation in the master and subproblems of the Benders' decomposition. The authors implement a two-phase algorithm for the Benders' decomposition and identify pareto-optimal cuts. The RRTAP includes costs due to flight delays, cancellation, and passenger rerouting, and the recovery stage includes cancellation, delay, and swapping options. To highlight the benefits of simultaneously solving planning and recovery problems in the RRTAP the authors compare their tail assignment solution against current approaches from the literature. Using airline data the authors demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption.

Language

  • English

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Filing Info

  • Accession Number: 01535331
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 12 2014 10:34AM