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A new bid price approach to dynamic resource allocation in network revenue management

Author

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  • Hosseinalifam, M.
  • Marcotte, P.
  • Savard, G.

Abstract

Firms selling perishable products use a variety of techniques to maximize revenue through the dynamic control of their inventories. One of the most powerful and simple approaches to address this issue consists of assigning threshold values (“bid prices”) to each resource, and to accept requests whenever their revenue exceeds the sum of the bid prices associated with its constituent resources. In this context, we propose a new customer choice-based mathematical program to estimate time-dependent bid prices. In contrast with most approaches from the current literature, ours is characterized by its flexibility. Indeed, it can easily embed technical and practical constraints that occur in most central reservation systems (CRS). To solve the model, we develop a column generation algorithm, in which the NP-hard subproblem is addressed via an efficient heuristic procedure. Our computational results illustrate the performance of the method, through comparisons with alternative proposals.

Suggested Citation

  • Hosseinalifam, M. & Marcotte, P. & Savard, G., 2016. "A new bid price approach to dynamic resource allocation in network revenue management," European Journal of Operational Research, Elsevier, vol. 255(1), pages 142-150.
  • Handle: RePEc:eee:ejores:v:255:y:2016:i:1:p:142-150
    DOI: 10.1016/j.ejor.2016.04.057
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    References listed on IDEAS

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    Cited by:

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    3. Laumer, Simon & Barz, Christiane, 2023. "Reductions of non-separable approximate linear programs for network revenue management," European Journal of Operational Research, Elsevier, vol. 309(1), pages 252-270.
    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. Neda Etebari Alamdari & Gilles Savard, 2021. "Deep reinforcement learning in seat inventory control problem: an action generation approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(5), pages 566-579, October.
    6. Lei Huang & Yandong Zhao & Liang Mei & Peiyi Wu & Zhihua Zhao & Yijun Mao, 2019. "Structural Holes in the Multi-Sided Market: A Market Allocation Structure Analysis of China’s Car-Hailing Platform in the Context of Open Innovation," Sustainability, MDPI, vol. 11(20), pages 1-20, October.

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