The Recoverable Robust Tail Assignment Problem
Froyland, G; Maher, SJ; Wu, C-L
Date: 25 June 2013
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Publisher DOI
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 ...
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 incorporating the Magnanti-Wong [21]
enhancement techniques. 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 with the tail assignment generated using a connection cost function
presented in Gr¨onkvist [15]. 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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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