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Computing Time-Dependent Bid Prices in Network Revenue Management Problems

Author

Listed:
  • Sumit Kunnumkal

    (Indian School of Business, Gachibowli, Hyderabad 500032, India)

  • Huseyin Topaloglu

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

Abstract

We propose a new method to compute bid prices in network revenue management problems. The novel aspect of our method is that it naturally provides dynamic bid prices that depend on how much time is left until departure. We show that our method provides an upper bound on the optimal total expected revenue and that this upper bound is tighter than the one provided by the widely known deterministic linear programming approach. Furthermore, it is possible to use the bid prices computed by our method as a starting point in a dynamic programming decomposition-like idea to decompose the network revenue management problem by the flight legs and to obtain dynamic and capacity-dependent bid prices. Our computational experiments indicate that the proposed method improves on many standard benchmarks.

Suggested Citation

  • Sumit Kunnumkal & Huseyin Topaloglu, 2010. "Computing Time-Dependent Bid Prices in Network Revenue Management Problems," Transportation Science, INFORMS, vol. 44(1), pages 38-62, February.
  • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:1:p:38-62
    DOI: 10.1287/trsc.1090.0291
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    References listed on IDEAS

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    Cited by:

    1. Guillermo Gallego & Richard Ratliff & Sergey Shebalov, 2015. "A General Attraction Model and Sales-Based Linear Program for Network Revenue Management Under Customer Choice," Operations Research, INFORMS, vol. 63(1), pages 212-232, February.
    2. Moussawi-Haidar, Lama & Nasr, Walid & Jalloul, Maya, 2021. "Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 275-291.
    3. Sumit Kunnumkal & Kalyan Talluri, 2011. "Equivalence of Piecewise-Linear Approximation and Lagrangian Relaxation for Network Revenue Management," Working Papers 608, Barcelona School of Economics.
    4. David Sayah, 2015. "Approximate Linear Programming in Network Revenue Management with Multiple Modes," Working Papers 1518, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Dong Li & Zhan Pang & Lixian Qian, 2023. "Bid price controls for car rental network revenue management," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 261-282, January.
    6. 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.
    7. Sumit Kunnumkal & Kalyan Talluri, 2011. "Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management," Economics Working Papers 1305, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2012.
    8. Sumit Kunnumkal & Kalyan Talluri, 2016. "On a Piecewise-Linear Approximation for Network Revenue Management," Mathematics of Operations Research, INFORMS, vol. 41(1), pages 72-91, February.
    9. Una McMahon-Beattie & Mairead McEntee & Robert McKenna & Ian Yeoman & Lynsey Hollywood, 2016. "Revenue management, pricing and the consumer," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 299-305, July.
    10. 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.
    11. Chaoxu Tong & Huseyin Topaloglu, 2014. "On the Approximate Linear Programming Approach for Network Revenue Management Problems," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 121-134, February.

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