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Network Revenue Management with Dependent Demands

  • Guillermo Gallego
  • Huseyin Topaloglu
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 279)

Abstract

Network revenue management models have traditionally been developed under the independent demand assumption. In the independent demand setting, customers arrive into the system with the intention to purchase a particular product. If this product is available, they purchase it. Otherwise, they leave the system. This model is reasonable when products are well differentiated so that customers do not substitute between products. The independent demand model is harder to justify when there are few differences, other than price, between fares. Indeed, a more general setting is needed when the demand for each product depends heavily on whether or not other products are available for sale. This setting gives the firms the opportunity to shape the demand for each product by adjusting the offer set made available to the customer.

References

  1. A. Aouad, R. Levi, D. Segev, Greedy-like algorithms for dynamic assortment planning under multinomial logit preferences. Oper. Res. 66 (5), 1321–1345 (2018c)Google Scholar
  2. A. Aouad, R. Levi, D. Segev, Approximation algorithms for dynamic assortment optimization models. Math. Oper. Res. 44(2), 487–511 (2019)Google Scholar
  3. S. Balseiro, C. Gocmen, R. Phillips, G. Gallego, Revenue management of consumer options for tournaments. Technical report, Columbia University, New York, NY (2010)Google Scholar
  4. F. Bernstein, A.G. Kok, L. Xie, Dynamic assortment customization with limited inventories. Manuf. Serv. Oper. Manag. 17 (4), 538–553 (2015)Google Scholar
  5. J.J.M. Bront, I. Mendez Diaz, G. Vulcano, A column generation algorithm for choice-based network revenue management. Oper. Res. 57 (3), 769–784 (2009)Google Scholar
  6. J. Chaneton, G. Vulcano, Computing bid-prices for revenue management under customer choice behavior. Manuf. Serv. Oper. Manag. 13 (4), 452–470 (2011)Google Scholar
  7. X. Chen, W. Ma, D. Simchi-Levi, L. Xin, Assortment planning for recommendations at checkout under inventory constraints. Technical report, MIT, Cambridge, MA (2016d)Google Scholar
  8. J. Dai, W. Ding, A. Kleywegt, X. Wang, Y. Zhang, Choice based revenue management for parallel flights. Technical report, Georgia Institute of Technology, Atlanta, GA (2015)Google Scholar
  9. J.B. Feldman, H. Topaloglu, Revenue management under the Markov chain choice model. Oper. Res. 65 (5), 1322–1342 (2017)Google Scholar
  10. G. Gallego, G. Iyengar, R. Phillips, A. Dubey, Managing flexible products on a network. Computational Optimization Research Center Technical Report TR-2004-01, Columbia University (2004)Google Scholar
  11. G. Gallego, R. Ratliff, S. Shebalov, A general attraction model and sales-based linear program for network revenue management under consumer choice. Oper. Res. 63 (1), 212–232 (2015)Google Scholar
  12. G. Gallego, A. Li, V.A. Truong, X. Wang, Online bipartite matching with customer choice. Technical report, Columbia University, New York, NY (2016b)Google Scholar
  13. V. Gaur, D. Honhon, Assortment planning and inventory decisions under a locational choice model. Manag. Sci. 52 (10), 1528–1543 (2006)Google Scholar
  14. N. Golrezaei, H. Nazerzadeh, P. Rusmevichientong, Real-time optimization of personalized assortments. Manag. Sci. 60 (6), 1532–1551 (2014)Google Scholar
  15. V. Goyal, R. Levi, D. Segev, Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Oper. Res. 64 (1), 219–235 (2016)Google Scholar
  16. D. Honhon, S. Seshadri, Fixed vs. random proportions demand models for the assortment planning problem under stockout-based substitution. Manuf. Serv. Oper. Manag. 15 (3), 378–386 (2013)Google Scholar
  17. D. Honhon, V. Gaur, S. Seshadri, Assortment planning and inventory decisions under stockout-based substitution. Oper. Res. 58 (5), 1364–1379 (2010)Google Scholar
  18. S. Jasin, S. Kumar, A re-solving heristic with bounded revenue loss for network revenue management with customer choice. Math. Oper. Res. 37 (2), 313–345 (2012)Google Scholar
  19. S. Kunnumkal, Randomization approaches for network revenue management with customer choice behavior. Prod. Oper. Manag. 23 (9), 1617–1633 (2014)Google Scholar
  20. S. Kunnumkal, K. Talluri, A strong Lagrangian relaxation for general discrete-choice network revenue management. Technical report, Indian School of Business, Hyderabad (2015a)Google Scholar
  21. S. Kunnumkal, K. Talluri, Choice network revenue management based on new compact formulations. Technical report, Indian School of Business, Hyderabad (2015b)Google Scholar
  22. S. Kunnumkal, H. Topaloglu, A refined deterministic linear program for the network revenue management problem with customer choice behavior. Nav. Res. Logist. 55 (6), 563–580 (2008)Google Scholar
  23. S. Kunnumkal, H. Topaloglu, A new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. Prod. Oper. Manag. 19 (5), 575–590 (2010a)Google Scholar
  24. S. Kunnumkal, H. Topaloglu, A randomized linear program for the network revenue management problem with customer choice behavior. J. Revenue Pricing Manag. 10 (5), 455–470 (2011a)Google Scholar
  25. Q. Liu, G. van Ryzin, On the choice-based linear programming model for network revenue management. Manuf. Serv. Oper. Manag. 10 (2), 288–310 (2008a)Google Scholar
  26. S. Mahajan, G.J. van Ryzin, Stocking retail assortments under dynamic consumer substitution. Oper. Res. 49 (3), 334–351 (2001)Google Scholar
  27. J. Meissner, A. Strauss, K. Talluri, An enhanced concave program relaxation for choice network revenue management. Prod. Oper. Manag. 22 (1), 71–87 (2013)Google Scholar
  28. D. Segev, Assortment planning with nested preferences: dynamic programming with distributions as states? Algorithmica 81 (1), 393–417 (2019)Google Scholar
  29. A.K. Strauss, K. Talluri, Tractable consideration set structures for assortment optimization and network revenue management. Prod. Oper. Manag. 26 (7), 1359–1368 (2017)Google Scholar
  30. K. Talluri, New formulations for choice network revenue management. INFORMS J. Comput. 26 (2), 401–413 (2014)Google Scholar
  31. H. Topaloglu, Joint stocking and product offer decisions under the multinomial logit model. Prod. Oper. Manag. 22 (5), 1182–1199 (2013)Google Scholar
  32. G.J. van Ryzin, S. Mahajan, On the relationship between inventory costs and variety benefits in retailassortments. Manag. Sci. 45 (11), 1496–1509 (1999)Google Scholar
  33. G.J. van Ryzin, G. Vulcano, Computing virtual nesting controls for network revenue management under customer choice behavior. Manuf. Serv. Oper. Manag. 10 (3), 448–467 (2008b)Google Scholar
  34. G. Vulcano, G.J. van Ryzin, W. Chaar, Choice-based revenue management: an empirical study of estimation and optimization. Manuf. Serv. Oper. Manag. 12 (3), 371–392 (2010)Google Scholar
  35. D. Zhang, An improved dynamic programming decomposition approach for network revenue management. Manuf. Serv. Oper. Manag. 13 (1), 35–52 (2011)Google Scholar
  36. D. Zhang, D. Adelman, An approximate dynamic programming approach to network revenue management with customer choice. Transp. Sci. 43 (3), 381–394 (2009)Google Scholar
  37. D. Zhang, W. Cooper, Revenue management for parallel flights with customer choice behavior. Oper. Res. 53 (3), 415–431 (2005)Google Scholar
  38. D. Zhang, T. Vossen, Reductions of approximate linear programs for network revenue management. Oper. Res. 63 (6), 1352–1371 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Guillermo Gallego
    • 1
  • Huseyin Topaloglu
    • 2
  1. 1.Clearwater BayHong Kong
  2. 2.ORIECornell UniversityNew YorkUSA

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