Enhancing robustness to forecast errors in availability control for airline revenue management

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Gonçalves, T (Author) Other; Bernardo Almada Lobo (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: Journal of Revenue and Pricing Management

ISSN: 1476-6930

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-010-B8V

DOI: 10.1057/s41272-024-00475-9

Abstract (EN): Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of ±20% can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by -10%. Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory. © The Author(s), under exclusive licence to Springer Nature Limited 2024.

Language: English

Type (Professor's evaluation): Scientific