Bayesian reinforcement learning to optimize paid ancillary revenue in the airline industry

Kevin Duijndam (), Ger Koole and Rob Mei
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Kevin Duijndam: Vrije Universiteit
Ger Koole: Vrije Universiteit
Rob Mei: Vrije Universiteit

Journal of Revenue and Pricing Management, 2025, vol. 24, issue 6, No 5, 567 pages

Abstract: Abstract To optimize the pricing of paid ancillary seats, we adopt a revenue management approach that optimizes over the capacity of these seats while accounting for unknown underlying model parameters. We test various models against a simulation model to assess the performance against wide-ranging input parameters. We demonstrate that using a Bayesian exponential demand model to describe the relationship between price and seats sold, combined with a Bayesian reinforcement learning approach to estimate its parameters, outperforms other approaches. By using a relatively simple demand model with a limited number of parameters, updating in a Bayesian manner, and in one step estimating demand parameters to directly use for price optimization, the model is quickly able to perform well across a wide range of demand scenarios.

Keywords: Dynamic pricing; Airline ancillaries; Ancillary pricing; Bayesian price optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41272-025-00523-y

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