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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. Guillermo Gallego & Robert Phillips, 2004. "Revenue Management of Flexible Products," Manufacturing & Service Operations Management, INFORMS, vol. 6(4), pages 321-337, January.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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.
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