Revenue-Utility Tradeoff in Assortment Optimization Under the Multinomial Logit Model with Totally Unimodular Constraints

Published Online:https://doi.org/10.1287/mnsc.2020.3657

References

  • Abeliuk A, Berbeglia G, Cebrian M, Van Hentenryck P (2016) Assortment optimization under a multinomial logit model with position bias and social influence. 4OR 14(1):57–75.Google Scholar
  • Adelman D, Mersereau AJ (2013) Dynamic capacity allocation to customers who remember past service. Management Sci. 59(3):592–612.LinkGoogle Scholar
  • Aflaki S, Popescu I (2014) Managing retention in service relationships. Management Sci. 60(2):415–433.LinkGoogle Scholar
  • Anupindi R, Gupta S, Venkataramanan MA (2015) Managing variety on the retail shelf: Using household scanner panel data to rationalize assortments. Agrawal N, Smith SA, eds. Retail Supply Chain Management: Quantitative Models and Empirical Studies (Springer US, Boston), 265–291.Google Scholar
  • Aouad A, Levi R, Segev D (2018) Greedy-like algorithms for dynamic assortment planning under multinomial logit preferences. Oper. Res. 66(5):1321–1345.LinkGoogle Scholar
  • Aouad A, Feldman J, Segev D, Zhang DJ (2019) Click-based MNL: Algorithmic frameworks for modeling click data in assortment optimization. Technical report, Washington University, St. Louis, MO.Google Scholar
  • Bront JJM, Mendez-Diaz I, Vulcano G (2009) A column generation algorithm for choice-based network revenue management. Oper. Res. 57(3):769–784.LinkGoogle Scholar
  • Calmon AP, Ciocan FD, Romero G (2018) Revenue management with repeated customer interactions. Technical report, University of Toronto, Toronto.Google Scholar
  • Carstensen PJ (1983) Complexity of some parametric integer and network programming problems. Math. Programming 26(1):64–75.CrossrefGoogle Scholar
  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Naval Res. Logist. Quart. 9(3–4):181–186.CrossrefGoogle Scholar
  • Chen KD, Hausman WH (2000) Technical note: Mathematical properties of the optimal product line selection problem using choice-based conjoint analysis. Management Sci. 46(2):327–332.LinkGoogle Scholar
  • Chen R, Jiang H (2017) Assortment optimization with position effects under the nested logit model. Technical report, Tsinghua University, Beijing.Google Scholar
  • Chen Y, Farias VF (2012) What’s on the table: Revenue management and the welfare gap in the US airline industry. Technical report, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Davis JM, Gallego G, Topaloglu H (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Unpublished technical report, Cornell University, Ithaca, NY.Google Scholar
  • Davis JM, Gallego G, Topaloglu H (2014) Assortment optimization under variants of the nested logit model. Oper. Res. 62(2):250–273.LinkGoogle Scholar
  • Derakhshan M, Golrezai N, Manshadi V, Mirrokni V (2019) Product ranking on online platforms. Technical report, Massachusetts Institute of Technology, Cambridge.Google Scholar
  • Desir A, Goyal V, Zhang J (2016) Near-optimal algorithms for capacity constrained assortment optimization. Technical report, Columbia University, New York.Google Scholar
  • Dong L, Kouvelis P, Tian Z (2009) Dynamic pricing and inventory control of substitute products. Manufacturing Service Oper. Management 11(2):317–339.LinkGoogle Scholar
  • Duch-Brown N, Grzybowski L, Romahn A, Verboven F (2017) The impact of online sales on consumers and firms: Evidence from consumer electronics. Internat. J. Indust. Organ. 52:30–62.CrossrefGoogle Scholar
  • Feldman JB, Topaloglu H (2015) Capacity constraints across nests in assortment optimization under the nested logit model. Oper. Res. 63(4):812–822.LinkGoogle Scholar
  • Feldman J, Zhang D, Liu X, Zhang N (2019) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Technical report, Washington University, St. Louis, MO.Google Scholar
  • Flores A, Berbeglia G, van Hentenryck P (2019) Assortment optimization under the sequential multinomial logit model. Eur. J. Oper. Res. 273(3):1052–1064.CrossrefGoogle Scholar
  • Gallego G, Wang R (2014) Multi-product price optimization and competition under the nested attraction model. Oper. Res. 62(2):450–461.LinkGoogle Scholar
  • Gallego G, Ratliff R, Shebalov S (2015) A general attraction model and sales-based linear programming formulation for network revenue management under customer choice. Oper. Res. 63(1):212–232.LinkGoogle Scholar
  • Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. CORC Technical Report TR-2004-01.Computational Optimization Research Center, New YorkGoogle Scholar
  • Gallego G, Li A, Truong VA, Wang X (2019) Approximation algorithms for product framing and pricing. Oper. Res. 68(1):134–160.LinkGoogle Scholar
  • Gans N (2002) Customer loyalty and supplier quality competition. Management Sci. 48(2):207–221.LinkGoogle Scholar
  • Gupta S, Kalmanje S, Kockelman KM (2006) Road pricing simulations: Traffic, land use and welfare impacts for Austin, Texas. Transportation Planning Tech. 29(1):1–23.CrossrefGoogle Scholar
  • Ho T-H, Park Y-H, Zhou Y-P (2006) Incorporating satisfaction into customer value analysis: Optimal investment in lifetime value. Marketing Sci. 25(3):260–277.LinkGoogle Scholar
  • Jagabathula S, Rusmevichientong P (2017) A nonparametric joint assortment and price choice model. Management Sci. 63(9):3128–3145.LinkGoogle Scholar
  • Kaggle (2016) Expedia hotel recommendations. Accessed August 5, 2019, https://www.kaggle.com/c/expedia-hotel-recommendations.Google Scholar
  • Kleinberg J, Tardos E (2005) Algorithm Design (Addison Wesley, New York).Google Scholar
  • Kling CL, Thomson CJ (1996) The implications of model specification for welfare estimation in nested logit models. Amer. J. Agricultural Econom. 78(1):103–114.CrossrefGoogle Scholar
  • L’Ecuyer P, Maille P, Stier-Moses NE, Tuffin B (2017) Revenue-maximizing rankings for online platforms with quality-sensitive consumers. Oper. Res. 65(2):408–423.LinkGoogle Scholar
  • Li G, Rusmevichientong P, Topaloglu H (2015) The d-level nested logit model: Assortment and price optimization problems. Oper. Res. 63(2):325–342.LinkGoogle 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.LinkGoogle Scholar
  • Li H, Huh WT (2015) Pricing under the nested attraction model with a multi-stage choice structure. Oper. Res. 63(4):840–850.LinkGoogle Scholar
  • Li H, Webster S (2017) Optimal pricing of correlated product options under the paired combinatorial logit model. Oper. Res. 65(5):1215–1230.LinkGoogle Scholar
  • McFadden D (1974) Conditional logit analysis of qualitative choice behavior. Zarembka P, ed. Frontiers in Economics. (Academic Press, New York), 105–142.Google Scholar
  • Mendez-Diaz I, Bront JJM, Vulcano G, Zabala P (2014) A branch-and-cut algorithm for the latent-class logit assortment problem. Discrete Appl. Math. 164(1):246–263.CrossrefGoogle Scholar
  • Nemhauser GL, Wolsey LA (1988) Integer and Combinatorial Optimization (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Puig-Junoy J, Saez M, Martinez-Garcia E (1998) Why do patients prefer hospital emergency visits? A nested multinomial logit analysis for patient-initiated contacts. Health Care Management Sci. 1(1):39–52.CrossrefGoogle Scholar
  • Quan TW, Williams KR (2018) Product variety, across-market demand heterogeneity, and the value of online retail. RAND J. Econom. 49(4):877–913.CrossrefGoogle Scholar
  • Rusmevichientong P, Shen Z-JM, Shmoys DB (2009) A PTAS for capacitated sum-of-ratios optimization. Oper. Res. Lett. 37(4):230–238.CrossrefGoogle 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.LinkGoogle Scholar
  • Rusmevichientong P, Van Roy B, Glynn P (2006) A nonparametric approach to multiproduct pricing. Oper. Res. 54(1):82–98.LinkGoogle Scholar
  • Rusmevichientong P, Shmoys D, Tong C, Topaloglu H (2014) Assortment optimization under the multinomial logit model with random choice parameters. Production Oper. Management 23(11):2023–2039.CrossrefGoogle Scholar
  • Sen A, Atamturk A, Kaminsky P (2018) A conic integer optimization approach to the constrained assortment problem under the mixed multinomial logit model. Oper. Res. 66(4):994–1003.LinkGoogle Scholar
  • Shi P (2019) Optimal priority-based allocation mechanisms. Technical report, University of Southern California, Los Angeles.Google Scholar
  • Song J-S, Xue Z (2007) Demand management and inventory control for substitutable products. Technical report, Duke University, Durham, NC.Google Scholar
  • Talluri K, van Ryzin GJ (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.LinkGoogle Scholar
  • Train K (2003) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Train K (2015) Welfare calculations in discrete choice models when anticipated and experienced attributes differ: A guide with examples. J. Choice Model. 16:15–22.CrossrefGoogle Scholar
  • Truong V-A (2014) Optimal selection of medical formularies. J. Revenue Pricing Management 13(2):113–132.CrossrefGoogle Scholar
  • Vanderbei RJ (2014) Linear Programming: Foundations and Extensions (Springer, New York).CrossrefGoogle Scholar
  • Vulcano G, van Ryzin G, Chaar W (2010) Choice-based revenue management: An empirical study of estimation and optimization. Manufacturing Service Oper. Management 12(3):371–392.LinkGoogle Scholar
  • Wang R (2012) Capacitated assortment and price optimization under the multinomial logit model. Oper. Res. Lett. 40(6):492–497.CrossrefGoogle Scholar
  • Wang R, Sahin O (2018) The impact of consumer search cost on assortment planning and pricing. Management Sci. 64(8):3649–3666.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.