IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v63y2015i1p212-232.html
   My bibliography  Save this article

A General Attraction Model and Sales-Based Linear Program for Network Revenue Management Under Customer Choice

Author

Listed:
  • Guillermo Gallego

    (Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

  • Richard Ratliff

    (Research Group, Sabre Holdings, Southlake, Texas 76092)

  • Sergey Shebalov

    (Research Group, Sabre Holdings, Southlake, Texas 76092)

Abstract

This paper addresses two concerns with the state of the art in network revenue management with dependent demands. The first concern is that the basic attraction model (BAM), of which the multinomial logit (MNL) model is a special case, tends to overestimate demand recapture in practice. The second concern is that the choice-based deterministic linear program, currently in use to derive heuristics for the stochastic network revenue management problem, has an exponential number of variables. We introduce a generalized attraction model (GAM) that allows for partial demand dependencies ranging from the BAM to the independent demand model (IDM). We also provide an axiomatic justification for the GAM and a method to estimate its parameters. As a choice model, the GAM is of practical interest because of its flexibility to adjust product-specific recapture. Our second contribution is a new formulation called the sales-based linear program (SBLP) that works for the GAM. This formulation avoids the exponential number of variables in the earlier choice-based network RM (revenue management) approaches and is essentially the same size as the well-known LP formulation for the IDM. The SBLP should be of interest to revenue managers because it makes choice-based network RM problems tractable to solve. In addition, the SBLP formulation yields new insights into the assortment problem that arises when capacities are infinite. Together these contributions move forward the state of the art for network revenue management under customer choice and competition.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:oropre:v:63:y:2015:i:1:p:212-232
    DOI: 10.1287/opre.2014.1328
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2014.1328
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.2014.1328?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
    ---><---

    References listed on IDEAS

    as
    1. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    2. 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.
    3. 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.
    4. Guillermo Gallego & Robert Phillips, 2004. "Revenue Management of Flexible Products," Manufacturing & Service Operations Management, INFORMS, vol. 6(4), pages 321-337, January.
    5. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    6. Sumit Kunnumkal & Huseyin Topaloglu, 2008. "A refined deterministic linear program for the network revenue management problem with customer choice behavior," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(6), pages 563-580, September.
    7. 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.
    8. 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.
    9. Juan José Miranda Bront & Isabel Méndez-Díaz & Gustavo Vulcano, 2009. "A Column Generation Algorithm for Choice-Based Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 769-784, June.
    10. Jeffrey P. Newman & Mark E. Ferguson & Laurie A. Garrow & Timothy L. Jacobs, 2014. "Estimation of Choice-Based Models Using Sales Data from a Single Firm," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 184-197, May.
    11. 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.
    12. 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.
    13. Stefanus Jasin & Sunil Kumar, 2012. "A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 313-345, May.
    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. 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.
    2. 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.
    3. Jacob B. Feldman & Huseyin Topaloglu, 2017. "Revenue Management Under the Markov Chain Choice Model," Operations Research, INFORMS, vol. 65(5), pages 1322-1342, October.
    4. Meissner, Joern & Strauss, Arne, 2012. "Network revenue management with inventory-sensitive bid prices and customer choice," European Journal of Operational Research, Elsevier, vol. 216(2), pages 459-468.
    5. Yuhang Ma & Paat Rusmevichientong & Mika Sumida & Huseyin Topaloglu, 2020. "An Approximation Algorithm for Network Revenue Management Under Nonstationary Arrivals," Operations Research, INFORMS, vol. 68(3), pages 834-855, May.
    6. Paat Rusmevichientong & Zuo-Jun Max Shen & David B. Shmoys, 2010. "Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint," Operations Research, INFORMS, vol. 58(6), pages 1666-1680, December.
    7. Paat Rusmevichientong & Huseyin Topaloglu, 2012. "Robust Assortment Optimization in Revenue Management Under the Multinomial Logit Choice Model," Operations Research, INFORMS, vol. 60(4), pages 865-882, August.
    8. 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.
    9. Dan Zhang, 2011. "An Improved Dynamic Programming Decomposition Approach for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 13(1), pages 35-52, April.
    10. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    11. 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.
    12. C. I. Chiang, 2023. "Availability control under online reviews in hospitality," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 385-398, October.
    13. 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.
    14. Guillermo Gallego & Huseyin Topaloglu, 2014. "Constrained Assortment Optimization for the Nested Logit Model," Management Science, INFORMS, vol. 60(10), pages 2583-2601, October.
    15. Guang Li & Paat Rusmevichientong & Huseyin Topaloglu, 2015. "The d -Level Nested Logit Model: Assortment and Price Optimization Problems," Operations Research, INFORMS, vol. 63(2), pages 325-342, April.
    16. 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.
    17. Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    18. Gönsch, Jochen & Koch, Sebastian & Steinhardt, Claudius, 2014. "Revenue management with flexible products: The value of flexibility and its incorporation into DLP-based approaches," International Journal of Production Economics, Elsevier, vol. 153(C), pages 280-294.
    19. Fernando Bernstein & A. Gürhan Kök & Lei Xie, 2015. "Dynamic Assortment Customization with Limited Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 538-553, October.
    20. Garrett van Ryzin & Gustavo Vulcano, 2015. "A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models," Management Science, INFORMS, vol. 61(2), pages 281-300, February.

    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:inm:oropre:v:63:y:2015:i:1:p:212-232. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.