Revenue Management Under the Markov Chain Choice Model

References

• Bishop C (2006) Pattern Recognition and Machine Learning (Springer, New York).Google Scholar
• Blanchet J, Gallego G, Goyal V (2013) A Markov chain approximation to choice modeling. Technical report, Columbia University, New York.Google Scholar
• Bront JJM, Diaz IM, Vulcano G (2009) A column generation algorithm for choice-based network revenue management. Oper. Res. 57(3):769–784.Link, Google Scholar
• Davis J, Gallego G, Topaloglu H (2014) Assortment optimization under variants of the nested logit model. Oper. Res. 62(2):250–273.Link, Google Scholar
• Desir A, Goyal V (2013) An FPTAS for capacity constrained assortment optimization. Technical report, Columbia University, New York.Google Scholar
• Farias VF, Jagabathula S, Shah D (2013) A non-parametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.Link, Google Scholar
• Gallego G, Topaloglu H (2014) Constrained assortment optimization for the nested logit model. Management Sci. 60(10):2583–2601.Link, Google Scholar
• Gallego G, Wang R (2014) Multi-product price optimization and competition under the nested attraction model. Oper. Res. 62(2):450–461.Link, Google Scholar
• Gallego G, Ratliff R, Shebalov S (2011) A general attraction model and an efficient formulation for the network revenue management problem. Technical report, Columbia University, New York.Google Scholar
• Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. Computational Optimization Research Center Technical Report TR-2004-01, Columbia University, New York.Google Scholar
• Jagabathula S (2008) Nonparametric choice modeling: Applications to operations management. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
• Kunnumkal S, Talluri K (2012) A new compact linear programming formulation for choice network revenue management. Technical report, Universitat Pompeu Fabra, Barcelona, Spain.Google Scholar
• Kunnumkal S, Topaloglu H (2008) A refined deterministic linear program for the network revenue management problem with customer choice behavior. Naval Res. Logist. Quart. 55(6):563–580.Crossref, Google Scholar
• Li G, Rusmevichientong P (2014) A greedy algorithm for assortment optimization in the two-level nested logit model. Oper. Res. Lett. 42(5):319–324.Crossref, Google Scholar
• Li H, Huh WT (2011) Pricing multiple products with the multinomial logit and nested models: Concavity and implications. Manufacturing Service Oper. Management 13(4):549–563.Link, Google Scholar
• Li G, Rusmevichientong P, Topaloglu H (2015) The d-level nested logit model: Assortment and price optimization problems. Oper. Res. 62(2):325–342.Link, Google Scholar
• Liu Q, van Ryzin G (2008) On the choice-based linear programming model for network revenue management. Manufacturing Service Oper. Management 10(2):288–310.Link, Google Scholar
• Meissner J, Strauss AK (2012) Network revenue management with inventory-sensitive bid prices and customer choice. Eur. J. Oper. Res. 216(2):459–468.Crossref, Google Scholar
• Meissner J, Strauss A, Talluri K (2012) An enhanced concave program relaxation for choice network revenue management. Production Oper. Management 22(1):71–87.Crossref, Google Scholar
• Mendez-Diaz I, Bront JJM, Vulcano G, Zabala P (2010) A branch-and-cut algorithm for the latent-class logit assortment problem. Discrete Appl. Math. 36:383–390.Google Scholar
• Puterman ML (1994) Markov Decision Processes (John Wiley & Sons, New York).Crossref, Google Scholar
• Rusmevichientong P, Shen Z-JM, Shmoys DB (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.Link, Google Scholar
• Rusmevichientong P, Shmoys DB, Tong C, Topaloglu H (2014) Assortment optimization under the multinomial logit model with random choice parameters. Production Oper. Management 23(11):2023–2039.Crossref, Google Scholar
• Talluri K (2011) A randomized concave programming method for choice network revenue management. Technical report, Universitat Pompeu Fabra, Barcelona, Spain.Google Scholar
• Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.Link, Google Scholar
• Train K (2003) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Vossen TWM, Zhang D (2013) Reductions of approximate linear programs for network revenue management. Technical report, University of Colorado at Boulder, Boulder.Google Scholar
• Vulcano G, van Ryzin GJ, Ratliff R (2012) Estimating primary demand for substitutable products from sales transaction data. Oper. Res. 60(2):313–334.Link, Google Scholar
• Wang R (2012) Capacitated assortment and price optimization under the multinomial logit model. Oper. Res. Lett. 40(6):492–497.Crossref, Google Scholar
• Wang R (2013) Assortment management under the generalized attraction model with a capacity constraint. J. Revenue and Pricing Management 12(3):254–270.Crossref, Google Scholar
• Zhang D, Adelman D (2009) An approximate dynamic programming approach to network revenue management with customer choice. Transportation Sci. 42(3):381–394.Link, Google Scholar
• Zhang D, Cooper WL (2005) Revenue management for parallel flights with customer choice behavior. Oper. Res. 53(3):415–431.Link, Google Scholar