Integration of turnaround and aircraft recovery to mitigate delay propagation in airline networks

This article presents a novel approach to incorporate the aircraft turnaround, which has recently been identified as one of the major contributors to airline delay, into existing concepts for integrated aircraft, crew, and passenger recovery. We aim to fill the research gap on how to holistically model network delay propagation as tactical decision support for airline schedule recovery. Our model introduces a heterogeneous vehicle routing problem with time windows for the assignment of aircraft to flight routes and integrates it with an extended version of the resource-constrained project schedule problem for the allocation of scarce resources to turnarounds at the central hub airport, such that we can proactively estimate delay propagation in an airline network. Passenger and crew itineraries are modelled as links between flights, such that needed transfer times influence the stand allocation and resource assignment. These links may only be broken if reserve capacities are available and the related rebooking and compensation costs are more efficient than accepting departure delays to maintain transfers. With this approach, we are able to calculate flight-specific delay cost functions and find substantial dependencies about the time of the day, the number of succeeding flight legs and particular downstream destinations. The integrated recovery model is implemented into a rolling horizon algorithm and applied to a case study setting to analyse its performance in comparison to the individual turnaround and aircraft recovery models. Within different delay scenarios, we find that the incorporation of turnaround recovery options significantly improves the resilience of the airline network. Especially in low and moderate delay situations, we achieve a full recovery of the flight schedule simply by rebooking passengers, reallocating aircraft among stands and accelerating ground operations. Thus, often considered recovery options, such as aircraft swaps and flight cancellations, are not required for delays around 30 min in our case study. This reduces total costs in comparison to the conventional aircraft recovery model by 49%. Despite the lower efficiency of turnaround recovery in medium and high delay scenarios, the combination of flexible aircraft assignments and ground operations still generates additional cost savings of at least 21% and helps to reduce the necessary amount of optimal recovery options. •Integration of aircraft routing and resource-constrained