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Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management

Author

Listed:
  • L. F. Escudero

    (Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, Madrid, Spain)

  • J. F. Monge

    (Centro de Investigación Operativa, Universidad Miguel Hernández, Elche (Alicante), Spain)

  • D. Romero Morales

    (Saïd Business School, University of Oxford, Oxford, United Kingdom)

  • J. Wang

    (Saïd Business School, University of Oxford, Oxford, United Kingdom)

Abstract

This paper studies a multistage stochastic programming (SP) model for large-scale network revenue management. We solve the model by means of the so-called expected future value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive approach. We compare our revenues to those obtained by additive bid prices, such as the bid prices derived from the deterministic equivalent model (DEM) of the compact representation of the SP model. Our computational experience shows that the revenues obtained by our approach are better for middle-range values of the load factor of demand, whereas the differences among all the approaches we have tested are insignificant for extreme values. Moreover, our approach requires significantly less computation time than does the optimization of DEM by plain use of optimization engines. Problem instances with 72 pairs of bundle-fare classes have been solved in less than one minute, with 800 pairs in less than five minutes, and with 4,000 pairs in less than one hour. The time taken by DEM was, in general, of one order of magnitude higher. Finally, for the three largest problem instances, and after two hours, the expected revenue returned by DEM was below that obtained by EFV by 13.47%, 17.14%, and 38.94%, respectively.

Suggested Citation

  • L. F. Escudero & J. F. Monge & D. Romero Morales & J. Wang, 2013. "Expected Future Value Decomposition Based Bid Price Generation for Large-Scale Network Revenue Management," Transportation Science, INFORMS, vol. 47(2), pages 181-197, May.
    • Handle: RePEc:inm:ortrsc:v:47:y:2013:i:2:p:181-197
    DOI: 10.1287/trsc.1120.0422
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    References listed on IDEAS

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    Cited by:

    1. Castro, Jordi & Escudero, Laureano F. & Monge, Juan F., 2023. "On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 268-285.
    2. 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.
    3. Aldasoro, Unai & Escudero, Laureano F. & Merino, María & Pérez, Gloria, 2017. "A parallel Branch-and-Fix Coordination based matheuristic algorithm for solving large sized multistage stochastic mixed 0–1 problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 590-606.
    4. Unai Aldasoro & Laureano Escudero & María Merino & Juan Monge & Gloria Pérez, 2015. "On parallelization of a stochastic dynamic programming algorithm for solving large-scale mixed 0–1 problems under uncertainty," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 703-742, October.

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