IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v15y2016i2d10.1057_rpm.2015.21.html
   My bibliography  Save this article

Itinerary-based nesting control with upsell

Author

Listed:
  • Chan Seng Pun
  • Diego Klabjan

    (Northwestern University)

  • Fikri Karaesmen

    (Koc University)

  • Sergey Shebalov

    (Sabre Holdings)

Abstract

In order to accept future high-yield booking requests, airlines protect seats from low-yield passengers. More seats may be reserved when passengers faced with closed fare classes can upsell to open higher fare classes. We address the airline revenue management problem with capacity nesting and customer upsell, and formulate this problem by a stochastic optimization model to determine a set of static protection levels for each itinerary. We apply an approximate dynamic programming framework to approximate the objective function by piecewise linear functions, whose slopes (marginal revenue) are iteratively updated and returned by an efficient heuristic that simultaneous handles both nesting and upsells. The resulting allocation policy is tested over a real airline network and benchmarked against the randomized linear programming bid-price policy under various demand settings. Simulation results suggest that the proposed allocation policy significantly outperforms when incremental demand or upsell probability are high. Structural analyses are also provided for special demand dependence cases.

Suggested Citation

  • Chan Seng Pun & Diego Klabjan & Fikri Karaesmen & Sergey Shebalov, 2016. "Itinerary-based nesting control with upsell," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(2), pages 107-137, April.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:2:d:10.1057_rpm.2015.21
    DOI: 10.1057/rpm.2015.21
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/rpm.2015.21
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/rpm.2015.21?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William L. Cooper, 2002. "Asymptotic Behavior of an Allocation Policy for Revenue Management," Operations Research, INFORMS, vol. 50(4), pages 720-727, August.
    2. Lijian Chen & Tito Homem-de-Mello, 2010. "Re-solving stochastic programming models for airline revenue management," Annals of Operations Research, Springer, vol. 177(1), pages 91-114, June.
    3. Kalyan Talluri & Garrett van Ryzin, 1998. "An Analysis of Bid-Price Controls for Network Revenue Management," Management Science, INFORMS, vol. 44(11-Part-1), pages 1577-1593, November.
    4. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    5. William L. Cooper & Diwakar Gupta, 2006. "Stochastic Comparisons in Airline Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 8(3), pages 221-234, February.
    6. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    7. de Boer, Sanne V. & Freling, Richard & Piersma, Nanda, 2002. "Mathematical programming for network revenue management revisited," European Journal of Operational Research, Elsevier, vol. 137(1), pages 72-92, February.
    8. Michael Z. F. Li & Tae H. Oum, 2002. "A Note on the Single Leg, Multifare Seat Allocation Problem," Transportation Science, INFORMS, vol. 36(3), pages 349-353, August.
    9. Warren Powell & Andrzej Ruszczyński & Huseyin Topaloglu, 2004. "Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 814-836, November.
    10. S. L. Brumelle & J. I. McGill & T. H. Oum & K. Sawaki & M. W. Tretheway, 1990. "Allocation of Airline Seats between Stochastically Dependent Demands," Transportation Science, INFORMS, vol. 24(3), pages 183-192, August.
    11. Topaloglu, Huseyin, 2009. "On the asymptotic optimality of the randomized linear program for network revenue management," European Journal of Operational Research, Elsevier, vol. 197(3), pages 884-896, September.
    12. S. L. Brumelle & J. I. McGill, 1993. "Airline Seat Allocation with Multiple Nested Fare Classes," Operations Research, INFORMS, vol. 41(1), pages 127-137, February.
    13. Garrett van Ryzin & Jeff McGill, 2000. "Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels," Management Science, INFORMS, vol. 46(6), pages 760-775, June.
    14. Kalyan Talluri & Garrett van Ryzin, 1999. "A Randomized Linear Programming Method for Computing Network Bid Prices," Transportation Science, INFORMS, vol. 33(2), pages 207-216, May.
    15. 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.
    16. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    17. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    18. Richard D. Wollmer, 1992. "An Airline Seat Management Model for a Single Leg Route When Lower Fare Classes Book First," Operations Research, INFORMS, vol. 40(1), pages 26-37, February.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    2. Dimitris Bertsimas & Sanne de Boer, 2005. "Simulation-Based Booking Limits for Airline Revenue Management," Operations Research, INFORMS, vol. 53(1), pages 90-106, February.
    3. Felix Papier, 2016. "Supply Allocation Under Sequential Advance Demand Information," Operations Research, INFORMS, vol. 64(2), pages 341-361, April.
    4. Dan Zhang & William L. Cooper, 2005. "Revenue Management for Parallel Flights with Customer-Choice Behavior," Operations Research, INFORMS, vol. 53(3), pages 415-431, June.
    5. William L. Cooper & Diwakar Gupta, 2006. "Stochastic Comparisons in Airline Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 8(3), pages 221-234, February.
    6. Dan Zhang & Larry Weatherford, 2017. "Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 18-35, February.
    7. E. Andrew Boyd & Ioana C. Bilegan, 2003. "Revenue Management and E-Commerce," Management Science, INFORMS, vol. 49(10), pages 1363-1386, October.
    8. Muzaffer Buyruk & Ertan Güner, 2022. "Personalization in airline revenue management: an overview and future outlook," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 129-139, April.
    9. William L. Cooper & Tito Homem-de-Mello, 2007. "Some Decomposition Methods for Revenue Management," Transportation Science, INFORMS, vol. 41(3), pages 332-353, August.
    10. Mihai Banciu & Fredrik Ødegaard & Alia Stanciu, 2019. "Distribution-free bounds for the expected marginal seat revenue heuristic with dependent demands," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(2), pages 155-163, April.
    11. 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.
    12. Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    13. Kavitha Balaiyan & R. K. Amit & Atul Kumar Malik & Xiaodong Luo & Amit Agarwal, 2019. "Joint forecasting for airline pricing and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 465-482, December.
    14. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2006. "Models of the Spiral-Down Effect in Revenue Management," Operations Research, INFORMS, vol. 54(5), pages 968-987, October.
    15. Zizhuo Wang & Yinyu Ye, 2016. "Hidden-City Ticketing: The Cause and Impact," Transportation Science, INFORMS, vol. 50(1), pages 288-305, February.
    16. Meissner, Joern & Strauss, Arne, 2012. "Improved bid prices for choice-based network revenue management," European Journal of Operational Research, Elsevier, vol. 217(2), pages 417-427.
    17. Feng, Youyi & Xiao, Baichun, 2006. "A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1298-1316, October.
    18. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    19. Georgia Perakis & Guillaume Roels, 2010. "Robust Controls for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 56-76, November.
    20. C. Barz & K. Waldmann, 2007. "Risk-sensitive capacity control in revenue management," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(3), pages 565-579, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:15:y:2016:i:2:d:10.1057_rpm.2015.21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.