Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning

Isaak L. Geursen, Bruno F. Santos, N. Yorke-Smith

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
86 Downloads (Pure)

Abstract

Current state-of-the-art airline planning models face computational limitations, restricting the operational applicability to problems of representative sizes. This is particularly the case when considering the uncertainty necessarily associated with the long-term plan of an aircraft fleet. Considering the growing interest in the application of machine learning techniques to operations research problems, this article investigates the applicability of these techniques for airline planning. Specifically, an Advantage Actor–Critic (A2C) reinforcement learning algorithm is developed for the airline fleet planning problem. The increased computational efficiency of using an A2C agent allows us to consider real-world-sized problems and account for highly-volatile uncertainty in demand and fuel price. The result is a multi-stage probabilistic fleet plan describing the evolution of the fleet according to a large set of future scenarios. The A2C algorithm is found to outperform a deterministic model and a deep Q-network algorithm. The relative performance of the A2C increases as more complexity is added to the problem. Further, the A2C algorithm can compute a multi-stage fleet planning solution within a few seconds
Original languageEnglish
Article number102397
Number of pages15
JournalJournal of Air Transport Management
Volume109
DOIs
Publication statusPublished - 2023

Funding

This research was partially supported by TAILOR, funded by the EU Horizon 2020 programme under grant 952215, and by Epistemic AI, funded by the EU Horizon 2020 programme under grant 964505.

Keywords

  • Airline fleet planning
  • Stochastic optimisation
  • Reinforcement learning
  • Advantage Actor–Critic
  • Fuel price uncertainty

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