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Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes

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  • Guanqun Ni

Abstract

Resource allocation problem is one of key issues in the field of revenue management. The traditional models usually rely on some restrictive assumptions about demand information or arrival process, which is sometimes out of line with reality. To overcome this shortcoming, the method of competitive analysis of online algorithms, which eliminates the need for the assumptions on demand and arrivals, is adopted to deal with the quantity-based revenue management problem. The current model in this paper considers two downgrade compatible levels of resources. Given the capacities and fares of both levels of resources, the objective is to accept appropriate customers and assign them to appropriate resources so as to maximize revenues. Compared with the existing literature, this paper generalizes the concerned resource allocation problem by considering multiple fares for each level of resources. From the perspective of online algorithms and competitive analysis, both an upper bound and an optimal online strategy are derived in this paper.

Suggested Citation

  • Guanqun Ni, 2022. "Competitive analysis of online revenue management with two hierarchical resources and multiple fare classes," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0276530
    DOI: 10.1371/journal.pone.0276530
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    References listed on IDEAS

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    1. Peter P. Belobaba, 1987. "Survey Paper---Airline Yield Management An Overview of Seat Inventory Control," Transportation Science, INFORMS, vol. 21(2), pages 63-73, May.
    2. Yingjie Lan & Huina Gao & Michael O. Ball & Itir Karaesmen, 2008. "Revenue Management with Limited Demand Information," Management Science, INFORMS, vol. 54(9), pages 1594-1609, September.
    3. Tak C. Lee & Marvin Hersh, 1993. "A Model for Dynamic Airline Seat Inventory Control with Multiple Seat Bookings," Transportation Science, INFORMS, vol. 27(3), pages 252-265, August.
    4. Marshall Fisher & Ananth Raman, 1996. "Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales," Operations Research, INFORMS, vol. 44(1), pages 87-99, February.
    5. Renwick E. Curry, 1990. "Optimal Airline Seat Allocation with Fare Classes Nested by Origins and Destinations," Transportation Science, INFORMS, vol. 24(3), pages 193-204, August.
    6. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    7. Lan, Yingjie & Ball, Michael O. & Karaesmen, Itir Z. & Zhang, Jean X. & Liu, Gloria X., 2015. "Analysis of seat allocation and overbooking decisions with hybrid information," European Journal of Operational Research, Elsevier, vol. 240(2), pages 493-504.
    8. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    9. Peter P. Belobaba, 1989. "OR Practice—Application of a Probabilistic Decision Model to Airline Seat Inventory Control," Operations Research, INFORMS, vol. 37(2), pages 183-197, April.
    10. Conrad J. Lautenbacher & Shaler Stidham, 1999. "The Underlying Markov Decision Process in the Single-Leg Airline Yield-Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 136-146, May.
    11. Lawrence W. Robinson, 1995. "Optimal and Approximate Control Policies for Airline Booking with Sequential Nonmonotonic Fare Classes," Operations Research, INFORMS, vol. 43(2), pages 252-263, April.
    12. Michael O. Ball & Maurice Queyranne, 2009. "Toward Robust Revenue Management: Competitive Analysis of Online Booking," Operations Research, INFORMS, vol. 57(4), pages 950-963, August.
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