An integrated scenario-based approach for robust aircraft routing, crew pairing and re-timing

Abstract

For reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (e.g. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decision-making process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. (2012) [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of Dunbar et al. (2012) [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially significant reduction in delay propagation. Gary FroylandSchool of Mathematics and Statistics, University of New South Wales, Sydney NSW 2052, Australia. Cheng-Lung WuSchool of Aviation, University of New South Wales, Sydney NSW 2052, Australia. AbstractFor reasons of tractability, the airline scheduling problem has traditionally been sequentially decomposed into various stages (eg. schedule generation, fleet assignment, aircraft routing, and crew pairing), with the decisions from one stage imposed upon the decisionmaking process in subsequent stages. Whilst this approach greatly simplifies the solution process, it unfortunately fails to capture the many dependencies between the various stages, most notably between those of aircraft routing and crew pairing, and how these dependencies affect the propagation of delays through the flight network. In Dunbar et al. [9] we introduced a new algorithm to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing. In this paper we extend the approach of [9] by proposing two new algorithms that achieve further improvements in delay propagation reduction via the incorporation of stochastic delay information. We additionally propose a heuristic, used in conjunction with these two approaches, capable of re-timing an incumbent aircraft and crew schedule to further minimize the cost of delay propagation. These algorithms provide promising results when applied to a real-world airline network and motivate our final integrated aircraft routing, crew pairing and re-timing approach which provides a substantially signif...

Comparison of two hybrid algorithms on incorporated aircraft routing and crew pairing problems

“…In [24] resolved the aircraft and crew scheduling iteratively begin from an insignificant expense and a progression of solutions that can increase the robustness. The incorporated model of aircraft routing, crew pairing and re-timing is presented by [25]. A heuristic method had been presented in the new approach which is can change the time of any aircraft and crew schedule for reducing the delay propagation.…”

Robust Aircraft Routing

“…Later on, Dunbar et al (2012) propose an improved solution approach to solve a deterministic integrated aircraft routing and crew pairing problem to minimize total propagated delay, given that flight leg delays are known to be constant. This work was recently extended to incorporate stochastic flight leg delay information (Dunbar et al 2014). They use historical flight leg delay data to construct random scenarios and develop two algorithms to solve the stochastic program.…”

“…In this work, we depart from previous methods (Lan et al 2006, Dunbar et al 2014) that use stochastic optimization to minimize the expectation of propagated delay. We instead utilize robust optimization to minimize the maximal possible propagated delay.…”

Developing a Model for Solving the Flight Perturbation Problem

“…according to the airline companies' schedule, the number of aircrafts is given as input and, considering other existing limitations of flight team such as their working hours and resting periods and also the objective function which is minimizing wage, a suitable crew is determined for flights. Crew's work schedule is determined by airliners so that all flights of the schedule are covered (Dunbar, Froyland & Wu, 2014;Saddoune, Desaulniers, Elhallaoui & Soumis, 2012).…”