Glomb L, Liers-Bergmann F, Rösel F (2024)
Publication Status: In press
Publication Type: Journal article, Original article
Future Publication Type: Journal article
Publication year: 2024
Publisher: Elsevier B.V.
City/Town: Radarweg 29, 1043 NX Amsterdam, Niederlande
Book Volume: 52
DOI: 10.1016/j.disopt.2024.100836
Airlines solve many different optimization problems and combine the resulting solutions to ensure smooth, minimum-cost operations. Crucial problems are the Fleet Assignment, which assigns aircraft types to flights of a given schedule, and the Tail Assignment, which determines individual flight sequences to be performed by single aircraft. In order to find a cost-optimal solution, many airlines use mathematical optimization models. For these to be effective, the available data and forecasts must reflect the situation as accurately as possible. However, especially in times of a pandemic, the underlying plan is subject to severe uncertainties: Staff and demand uncertainties can even lead to flight cancellations or result in entire aircraft having to be grounded. Therefore, it is advantageous for airlines to protect their mathematical models against uncertainties in the input parameters. In this work, two computational tractable and cost-efficient robust models and solution approaches are developed: First, we set up a novel mixed integer model for the integrated fleet and tail assignment, which ensures that as few subsequent flights as possible have to be canceled in the event of initial flight cancellations. We then solve this model using a procedure that ensures that the costs of the solution remain considerably low. Our second model is an extended fleet assignment model that allows us to compensate for entire aircraft cancellations in the best possible way, taking into account rescheduling options. We demonstrate the effectiveness of both approaches by conducting an extensive computational study based on real flight schedules of a major German airline. It turns out that both models deliver stable, cost-efficient solutions within less than ten minutes, which significantly reduce follow-up costs in the case uncertainties arise.
APA:
Glomb, L., Liers-Bergmann, F., & Rösel, F. (2024). Fleet & tail assignment under uncertainty. Discrete Optimization, 52. https://dx.doi.org/10.1016/j.disopt.2024.100836
MLA:
Glomb, Lukas, Frauke Liers-Bergmann, and Florian Rösel. "Fleet & tail assignment under uncertainty." Discrete Optimization 52 (2024).
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