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A deep reinforcement learning approach to seat inventory control for airline revenue management

Author

Listed:
  • Syed A. M. Shihab

    (Kent State University)

  • Peng Wei

    (George Washington University)

Abstract

Commercial airlines use revenue management systems to maximize their revenue by making real-time decisions on the booking limits of different fare classes offered in each of its scheduled flights. Traditional approaches—such as mathematical programming, dynamic programming, and heuristic rule-based decision models—heavily rely on external mathematical models of demand and passenger arrival, choice, and cancelation, making their performance sensitive to the accuracy of these model estimates. Moreover, many of these approaches scale poorly with increase in problem dimensionality. Additionally, they lack the ability to explore and “directly” learn the true market dynamics from interactions with passengers and adapt to changes in market conditions on their own. To overcome these limitations, this research uses deep reinforcement learning (DRL), a model-free decision-making framework, for finding the optimal policy of the seat inventory control problem. The DRL framework employs a deep neural network to approximate the expected optimal revenues for all possible state-action combinations, allowing it to handle the large state space of the problem. Multiple fare classes with stochastic demand, passenger arrivals, and booking cancelations have been considered in the problem. An air travel market simulator was developed based on the market dynamics and passenger behavior for training and testing the agent. The results demonstrate that the DRL agent is capable of learning the optimal airline revenue management policy through interactions with the market, matching the performance of exact dynamic programming methods. The revenue generated by the agent in different simulated market scenarios was found to be close to the maximum possible flight revenues and surpass that produced by the expected marginal seat revenue-b (EMSRb) method.

Suggested Citation

  • Syed A. M. Shihab & Peng Wei, 2022. "A deep reinforcement learning approach to seat inventory control for airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 183-199, April.
  • Handle: RePEc:pal:jorapm:v:21:y:2022:i:2:d:10.1057_s41272-021-00281-7
    DOI: 10.1057/s41272-021-00281-7
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    References listed on IDEAS

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    1. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    2. Peter P Belobaba, 2016. "Optimization models in RM systems: Optimality versus revenue gains," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 229-235, July.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Martin J. Beckmann & F. Bobkoski, 1958. "Airline demand: An analysis of some frequency distributions," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 5(1), pages 43-51, March.
    5. Iliescu, Dan C. & Garrow, Laurie A. & Parker, Roger A., 2008. "A hazard model of US airline passengers' refund and exchange behavior," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 229-242, March.
    6. Lawrence R. Weatherford & Samuel E. Bodily & Phillip E. Pfeifer, 1993. "Modeling the Customer Arrival Process and Comparing Decision Rules in Perishable Asset Revenue Management Situations," Transportation Science, INFORMS, vol. 27(3), pages 239-251, August.
    7. Dimitris Bertsimas & Sanne de Boer, 2005. "Simulation-Based Booking Limits for Airline Revenue Management," Operations Research, INFORMS, vol. 53(1), pages 90-106, February.
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