Reinforcement learning applied to airline revenue management

Nicolas Bondoux (), Anh Quan Nguyen (), Thomas Fiig () and Rodrigo Acuna-Agost ()
Additional contact information
Nicolas Bondoux: Research, Innovation and Ventures, Amadeus S.A.S.
Anh Quan Nguyen: Research, Innovation and Ventures, Amadeus S.A.S.
Thomas Fiig: Amadeus IT Group
Rodrigo Acuna-Agost: Research, Innovation and Ventures, Amadeus S.A.S.

Journal of Revenue and Pricing Management, 2020, vol. 19, issue 5, No 6, 332-348

Abstract: Abstract Reinforcement learning (RL) is an area of machine learning concerned with how agents take actions to optimize a given long-term reward by interacting with the environment they are placed in. Some well-known recent applications include self-driving cars and computers playing games with super-human performance. One of the main advantages of this approach is that there is no need to explicitly model the nature of the interactions with the environment. In this work, we present a new airline Revenue Management System (RMS) based on RL, which does not require a demand forecaster. The optimization module remains but works in a different way. It is theoretically proven that RL converges to the optimal solution; however, in practice, the system may require a significant amount of data (a booking history with millions of daily departures) to learn the optimal policies. To overcome these difficulties, we present a novel model that integrates domain knowledge with a deep neural network trained on GPUs. The results are very encouraging in different scenarios and open the door for a new generation of RMSs that could automatically learn by directly interacting with customers.

Keywords: Revenue Management System; Machine Learning; Reinforcement Learning; Deep Reinforcement Learning; Q-Learning; Deep Q-Learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1057/s41272-020-00228-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorapm:v:19:y:2020:i:5:d:10.1057_s41272-020-00228-4

Ordering information: This journal article can be ordered from
https://www.palgrave.com/gp/journal/41272

DOI: 10.1057/s41272-020-00228-4

Access Statistics for this article

Journal of Revenue and Pricing Management is currently edited by Ian Yeoman

More articles in Journal of Revenue and Pricing Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().