IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v17y2018i6d10.1057_s41272-018-0149-x.html
   My bibliography  Save this article

A dynamic pricing engine for multiple substitutable flights

Author

Listed:
  • Michael D. Wittman

    (International Center for Air Transportation
    Amadeus Airline IT)

  • Thomas Fiig

    (Amadeus Airline IT)

  • Peter P. Belobaba

    (International Center for Air Transportation)

Abstract

As enhancements in airline IT begin to expand pricing and revenue management (RM) capabilities, airlines are starting to develop dynamic pricing engines (DPEs) to dynamically adjust the fares that would normally be offered by existing pricing and RM systems. In past work, simulations have found that DPEs can lead to revenue gains for airlines over traditional pricing and RM. However, these algorithms typically price each itinerary independently without directly considering the attributes and availability of other alternatives. In this paper, we introduce a dynamic pricing engine that simultaneously prices multiple substitutable itineraries that depart at different times. Using a Hotelling line (also called a locational choice model) to represent customer tradeoffs between departure times and price, the DPE dynamically suggests increments or decrements to the prices of pre-determined fare products as a function of booking request characteristics, departure time preferences, and the airline’s estimates of customer willingness-to-pay. Simulations in the Passenger Origin–Destination Simulator (PODS) show that simultaneous dynamic pricing can result in revenue gains of between 5 and 7% over traditional RM when used in a simple network with one airline and two flights. The heuristic produces revenue gains by stimulating new bookings, encouraging business passenger buy-up, and leading to spiral-up of forecast demand. However, simultaneous dynamic pricing produces marginal gains of less than 1% over a DPE that prices each itinerary independently. Given the complexity of specifying and implementing a simultaneous pricing model in practice, practitioners may prefer to use a flight-by-flight approach when developing DPEs.

Suggested Citation

  • Michael D. Wittman & Thomas Fiig & Peter P. Belobaba, 2018. "A dynamic pricing engine for multiple substitutable flights," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(6), pages 420-435, December.
  • Handle: RePEc:pal:jorapm:v:17:y:2018:i:6:d:10.1057_s41272-018-0149-x
    DOI: 10.1057/s41272-018-0149-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-018-0149-x
    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/s41272-018-0149-x?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. Dan Zhang & Zhaosong Lu, 2013. "Assessing the Value of Dynamic Pricing in Network Revenue Management," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 102-115, February.
    2. Seelhorst, Michael & Liu, Yi, 2015. "Latent air travel preferences: Understanding the role of frequent flyer programs on itinerary choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 49-61.
    3. Zhang, Dan & Cooper, William L., 2009. "Pricing substitutable flights in airline revenue management," European Journal of Operational Research, Elsevier, vol. 197(3), pages 848-861, September.
    4. Adam Bockelie & Peter Belobaba, 2017. "Incorporating ancillary services in airline passenger choice models," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 553-568, December.
    5. Peter P Belobaba, 2016. "Optimization models in RM systems: Optimality versus revenue gains," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 229-235, July.
    6. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    7. Vishal Gaur & Dorothée Honhon, 2006. "Assortment Planning and Inventory Decisions Under a Locational Choice Model," Management Science, INFORMS, vol. 52(10), pages 1528-1543, October.
    8. Yalç{i}n Akçay & Harihara Prasad Natarajan & Susan H. Xu, 2010. "Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice," Management Science, INFORMS, vol. 56(8), pages 1345-1361, August.
    9. Thomas Fiig & Oriana Goyons & Robin Adelving & Barry Smith, 2016. "Dynamic pricing – The next revolution in RM?," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 360-379, October.
    10. Minsuk Suh & Goker Aydin, 2011. "Dynamic pricing of substitutable products with limited inventories under logit demand," IISE Transactions, Taylor & Francis Journals, vol. 43(5), pages 323-331.
    11. Guillermo Gallego & Ruxian Wang, 2014. "Multiproduct Price Optimization and Competition Under the Nested Logit Model with Product-Differentiated Price Sensitivities," Operations Research, INFORMS, vol. 62(2), pages 450-461, April.
    12. Chen, Shaoxiang & Gallego, Guillermo & Li, Michael Z.F. & Lin, Bing, 2010. "Optimal seat allocation for two-flight problems with a flexible demand segment," European Journal of Operational Research, Elsevier, vol. 201(3), pages 897-908, March.
    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. Ming Chen & Zhi-Long Chen, 2018. "Robust Dynamic Pricing with Two Substitutable Products," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 249-268, May.
    2. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    3. Schlicher, Loe & Lurkin, Virginie, 2022. "Stable allocations for choice-based collaborative price setting," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1242-1254.
    4. 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.
    5. Jalali, Hamed & Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Boute, Robert, 2019. "Quality and pricing decisions in production/inventory systems," European Journal of Operational Research, Elsevier, vol. 272(1), pages 195-206.
    6. Zhenzhen Yan & Karthik Natarajan & Chung Piaw Teo & Cong Cheng, 2022. "A Representative Consumer Model in Data-Driven Multiproduct Pricing Optimization," Management Science, INFORMS, vol. 68(8), pages 5798-5827, August.
    7. Hongmin Li & Scott Webster, 2017. "Optimal Pricing of Correlated Product Options Under the Paired Combinatorial Logit Model," Operations Research, INFORMS, vol. 65(5), pages 1215-1230, October.
    8. James M. Davis & Huseyin Topaloglu & David P. Williamson, 2017. "Pricing Problems Under the Nested Logit Model with a Quality Consistency Constraint," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 54-76, February.
    9. W. Zachary Rayfield & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "Approximation Methods for Pricing Problems Under the Nested Logit Model with Price Bounds," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 335-357, May.
    10. Anton J. Kleywegt & Hongzhang Shao, 2022. "Revenue Management Under the Markov Chain Choice Model with Joint Price and Assortment Decisions," Papers 2204.04774, arXiv.org.
    11. Hongmin Li & Scott Webster & Gwangjae Yu, 2020. "Product Design Under Multinomial Logit Choices: Optimization of Quality and Prices in an Evolving Product Line," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1011-1025, September.
    12. Ruben van de Geer & Arnoud V. den Boer, 2022. "Price Optimization Under the Finite-Mixture Logit Model," Management Science, INFORMS, vol. 68(10), pages 7480-7496, October.
    13. Chenhao Du & William L. Cooper & Zizhuo Wang, 2016. "Optimal Pricing for a Multinomial Logit Choice Model with Network Effects," Operations Research, INFORMS, vol. 64(2), pages 441-455, April.
    14. Shin, Hojung & Park, Soohoon & Lee, Euncheol & Benton, W.C., 2015. "A classification of the literature on the planning of substitutable products," European Journal of Operational Research, Elsevier, vol. 246(3), pages 686-699.
    15. Zhang Wensi & Li Jinlin & Ran Lun, 2014. "Price Increasing Timing of Competitive Perishable Products," Journal of Systems Science and Information, De Gruyter, vol. 2(1), pages 29-37, February.
    16. Aparna Jayaram & R. K. Amit & Amit Agarwal & Xiaodong Luo, 2024. "Elasticity-integrated pricing and allocation heuristic for airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(4), pages 305-317, August.
    17. Lebedev, Denis & Goulart, Paul & Margellos, Kostas, 2021. "A dynamic programming framework for optimal delivery time slot pricing," European Journal of Operational Research, Elsevier, vol. 292(2), pages 456-468.
    18. Joseph Jiaqi Xu & Peter S. Fader & Senthil Veeraraghavan, 2019. "Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets," Service Science, INFORMS, vol. 21(1), pages 121-138, January.
    19. Sentao Miao & Xiuli Chao, 2021. "Dynamic Joint Assortment and Pricing Optimization with Demand Learning," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 525-545, March.
    20. Farhad Etebari, 2020. "Pricing Competition Under Specific Discrete Choice Models," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(02), pages 1-14, March.

    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:17:y:2018:i:6:d:10.1057_s41272-018-0149-x. 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.