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A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem

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  • Huang, Kuancheng
  • Liang, Yu-Tung

Abstract

Since American Airlines successfully applied revenue management (RM) to raise its revenue, RM has become a common technique in the airline industry. Due to the current hub-and-spoke operation of the airline industry, the focus of RM research has shifted from the traditional single-leg problem to the network-type problem. The mainstream approaches, bid price and virtual nesting, are faced with some limitations such as inaccuracy due to their suboptimal nature and operation interruption caused by the required updates. This study developed an algorithm to generate a seat control policy by approximating the expected revenue function in a dynamic programming (DP) model. In order to deal with the issue of dimensionality for the DP model in a network context, this study used a suitable parameterized function and a sampling concept to achieve the approximation. In the numerical experiment, the objective function value of the developed algorithm was very close to the one achieved by the optimal control. We believe that this approach can serve as an alternative to the current mainstream approaches for the network RM problem for airlines and will provide an inspiring concept for other types of multi-resource RM problems.

Suggested Citation

  • Huang, Kuancheng & Liang, Yu-Tung, 2011. "A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(3), pages 333-341, May.
    • Handle: RePEc:eee:transe:v:47:y:2011:i:3:p:333-341
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    Citations

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    Cited by:

    1. Huang, Kuancheng & Lin, Chia-Yi, 2014. "A simulation analysis for the re-solving issue of the network revenue management problem," Journal of Air Transport Management, Elsevier, vol. 38(C), pages 36-42.
    2. Wang, Shuaian & Wang, Hua & Meng, Qiang, 2015. "Itinerary provision and pricing in container liner shipping revenue management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 135-146.
    3. Asadi, Amin & Nurre Pinkley, Sarah, 2021. "A stochastic scheduling, allocation, and inventory replenishment problem for battery swap stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    4. Wuyang Yuan & Lei Nie & Xin Wu & Huiling Fu, 2018. "A dynamic bid price approach for the seat inventory control problem in railway networks with consideration of passenger transfer," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    5. Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Qu, Xiaobo, 2021. "A two-stage stochastic nonlinear integer-programming model for slot allocation of a liner container shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 143-160.
    6. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    7. Zhen, Lu & Wang, Shuaian & Zhuge, Dan, 2017. "Dynamic programming for optimal ship refueling decision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 63-74.
    8. Alavi Fard, Farzad & Sy, Malick & Ivanov, Dmitry, 2019. "Optimal overbooking strategies in the airlines using dynamic programming approach in continuous time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 384-399.

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