The Recoverable Robust Tail Assignment Problem

Gary Froyland (), Stephen J. Maher () and Cheng-Lung Wu ()
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Gary Froyland: School of Mathematics and Statistics, University of New South Wales, Sydney NSW 2052, Australia
Stephen J. Maher: School of Mathematics and Statistics, University of New South Wales, Sydney NSW 2052, Australia
Cheng-Lung Wu: School of Aviation, University of New South Wales, Sydney NSW 2052, Australia

Transportation Science, 2014, vol. 48, issue 3, 351-372

Abstract: 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. We propose a recoverable robustness technique as an alternative to robust optimisation to reduce the effect of disruptions and the cost of recovery. We 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. We 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 we compare our tail assignment solution against current approaches from the literature. Using airline data we demonstrate that by developing a better tail assignment plan via the RRTAP framework, one can reduce recovery costs in the event of a disruption.

Keywords: robust airline optimisation; recovery; Benders' decomposition (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (11)

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