Robust Airline Schedule Planning: Minimizing Propagated Delay in an Integrated Routing and Crewing Framework

Abstract

For 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 decision making 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. As delays are commonly transferred between late running aircraft and crew, it is important that aircraft routing and crew pairing decisions are made together. The propagated delay may then be accurately estimated to minimize the overall propagated delay for the network and produce a robust solution for both aircraft and crew. In this paper we introduce a new approach to accurately calculate and minimize the cost of propagated delay, in a framework that integrates aircraft routing and crew pairing.

Modeling and Solving Vehicle Routing Problems with Many Available Vehicle Types

“…Branching on variables has not been performed in this thesis, however it would be an interesting extention of the implemented algorithm. The review [16] …”

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

“…In Section 3 we outline a re-timing heuristic that improves upon the shortcomings of the existing re-timing approaches mentioned above via the simultaneous re-timing of aircraft and crew and in such a way as to minimize delay propagation between aircraft and crew. In Section 4 we extend the integrated aircraft routing and crew pairing model of Dunbar et al [9] by proposing two alternative approaches for incorporating delay scenarios within the aircraft routing and crew pairing problems and outline an approach for integrating this with the improved heuristic. This inclusion of multiple delay scenarios allows an airline to incorporate historical primary delay information into the model in a more meaningful way, rather than simply making use of expected delays.…”

“…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.…”

“…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.…”

Robust Airline Scheduling with Controllable Cruise Times and Chance Constraints

“…Arıkan and Deshpande (2012) analyzed the impact of scheduled block times on on-time performance. Dunbar et al (2012) presented a mathematical model to minimize propagated delay costs while integrating aircraft routing and crew pairing problems. Other researchers have addressed the problem of slack distribution and its effects on schedule performances.…”