Randomized Assortment Optimization

Published Online:https://doi.org/10.1287/opre.2022.0129

References

  • Aouad A, Farias V, Levi R (2021) Assortment optimization under consider-then-choose choice models. Management Sci. 67(6):3368–3386.LinkGoogle Scholar
  • Aouad A, Farias V, Levi R, Segev D (2018) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.LinkGoogle Scholar
  • Bandi C, Bertsimas D (2012) Tractable stochastic analysis in high dimensions via robust optimization. Math. Programming 134:23–70.CrossrefGoogle Scholar
  • Baron O, Milner J, Naseraldin H (2011) Facility location: A robust optimization approach. Production Oper. Management 20(5):772–785.CrossrefGoogle Scholar
  • Bayraksan G, Love DK (2015) Data-driven stochastic programming using phi-divergences. Aleman DM, Thiele AC, eds. The Operations Research Revolution, INFORMS Tutorials in Operations Research (INFORMS, Catonsville, MD), 1–19.Google Scholar
  • Belloni A, Freund R, Selove M, Simester D (2008) Optimizing product line designs: Efficient methods and comparisons. Management Sci. 54(9):1544–1552.LinkGoogle Scholar
  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization (Princeton University Press, Princeton, NJ).CrossrefGoogle Scholar
  • Bertsimas D, Caramanis C (2010) Finite adaptability in multistage linear optimization. IEEE Trans. Automated Control 55(12):2751–2766.CrossrefGoogle Scholar
  • Bertsimas D, Mišić V (2017) Robust product line design. Oper. Res. 65(1):19–37.LinkGoogle Scholar
  • Bertsimas D, Mišić V (2019) Exact first-choice product line optimization. Oper. Res. 67(3):651–670.LinkGoogle Scholar
  • Bertsimas D, Sim M (2004) The price of robustness. Oper. Res. 52(1):35–53.LinkGoogle Scholar
  • Bertsimas D, Thiele A (2006) A robust optimization approach to inventory theory. Oper. Res. 54(1):150–168.LinkGoogle Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.CrossrefGoogle Scholar
  • Bertsimas D, Nasrabadi E, Orlin JB (2016) On the power of randomization in network interdiction. Oper. Res. Lett. 44(1):114–120.CrossrefGoogle Scholar
  • Billingsley P (1961) Statistical Inference for Markov Processes (University of Chicago Press, Chicago).Google Scholar
  • Birbil Şİ, Frenk J, Gromicho JA, Zhang S (2009) The role of robust optimization in single-leg airline revenue management. Management Sci. 55(1):148–163.LinkGoogle Scholar
  • Bishop CM (2006) Pattern Recognition and Machine Learning (Springer, Berlin).Google Scholar
  • Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.LinkGoogle Scholar
  • Bumpensanti P, Wang H (2020) A re-solving heuristic with uniformly bounded loss for network revenue management. Management Sci. 66(7):2993–3009.LinkGoogle Scholar
  • Davis J, Gallego G, Topaloglu H (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Working paper, Cornell University, Ithaca, NY.Google Scholar
  • Davis J, Gallego G, Topaloglu H (2014) Assortment optimization under variants of the nested logit model. Oper. Res. 62(2):250–273.LinkGoogle Scholar
  • Delage E, Saif A (2022) The value of randomized solutions in mixed-integer distributionally robust optimization problems. INFORMS J. Comput. 34(1):333–353.LinkGoogle Scholar
  • Delage E, Kuhn D, Wiesemann W (2019) “Dice”-sion-making under uncertainty: When can a random decision reduce risk? Management Sci. 65(7):3282–3301.Google Scholar
  • DeMiguel V, Nogales FJ (2009) Portfolio selection with robust estimation. Oper. Res. 57(3):560–577.LinkGoogle Scholar
  • Désir A, Goyal V, Segev D, Ye C (2020) Constrained assortment optimization under the Markov chain–based choice model. Management Sci. 66(2):698–721.LinkGoogle Scholar
  • Désir A, Goyal V, Jiang B, Xie T, Zhang J (2023) Robust assortment optimization under the Markov chain choice model. Oper. Res., epub ahead of print January 13, https://doi.org/10.1287/opre.2022.2420.LinkGoogle Scholar
  • El Ghaoui L, Lebret H (1997) Robust solutions to least-squares problems with uncertain data. SIAM J. Matrix Anal. Appl. 18(4):1035–1064.CrossrefGoogle Scholar
  • Farias V, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.Google Scholar
  • Feldman JB, Topaloglu H (2017) Revenue management under the Markov chain choice model. Oper. Res. 65(5):1322–1342.Google Scholar
  • Ferreira KJ, Simchi-Levi D, Wang H (2018) Online network revenue management using Thompson sampling. Oper. Res. 66(6):1586–1602.LinkGoogle Scholar
  • Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics (Springer, Berlin).CrossrefGoogle Scholar
  • Goldfarb D, Iyengar G (2003) Robust portfolio selection problems. Math. Oper. Res. 28(1):1–38.LinkGoogle Scholar
  • Hanasusanto G, Kuhn D, Wiesemann W (2015) K-adaptability in two-stage robust binary programming. Oper. Res. 63(4):877–891.LinkGoogle Scholar
  • Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin).CrossrefGoogle Scholar
  • Honhon D, Jonnalagedda S, Pan XA (2012) Optimal algorithms for assortment selection under ranking-based consumer choice models. Manufacturing Service Oper. Management 14(2):279–289.LinkGoogle Scholar
  • Jasin S (2015) Performance of an LP-based control for revenue management with unknown demand parameters. Oper. Res. 63(4):909–915.LinkGoogle Scholar
  • Jasin S, Kumar S (2012) A re-solving heuristic with bounded revenue loss for network revenue management with customer choice. Math. Oper. Res. 37(2):313–345.LinkGoogle Scholar
  • Kök AG, Fisher ML, Vaidyanathan R (2015) Assortment planning: Review of literature and industry practice. Agrawal N, Smith S, eds., Retail Supply Chain Management: Quantitative Models and Empirical Studies (Springer, Berlin), 175–236.CrossrefGoogle Scholar
  • Luce RD (1959) Individual Choice Behavior: A Theoretical Analysis (Courier Corporation, North Chelmsford, MA).Google Scholar
  • Ma W (2023) When is assortment optimization optimal? Management Sci. 69(4):2088–2105.LinkGoogle Scholar
  • Mahajan S, van Ryzin G (2001) Stocking retail assortments under dynamic consumer substitution. Oper. Res. 49(3):334–351.LinkGoogle Scholar
  • Mak HY, Rong Y, Zhang J (2015) Appointment scheduling with limited distributional information. Management Sci. 61(2):316–334.LinkGoogle Scholar
  • Michaud RO (1989) The Markowitz optimization enigma: Is ‘optimized’ optimal? Financial Anal. J. 45(1):31–42.CrossrefGoogle Scholar
  • Paul A, Feldman J, Davis J (2018) Assortment optimization and pricing under a nonparametric tree choice model. Manufacturing Service Oper. Management 20(3):550–565.LinkGoogle Scholar
  • Perakis G, Roels G (2010) Robust controls for network revenue management. Manufacturing Service Oper. Management 12(1):56–76.LinkGoogle Scholar
  • Plackett RL (1975) The analysis of permutations. J. Roy. Statist. Soc. Ser. C Appl. Statist. 24(2):193–202.Google Scholar
  • Reiman MI, Wang Q (2008) An asymptotically optimal policy for a quantity-based network revenue management problem. Math. Oper. Res. 33(2):257–282.LinkGoogle Scholar
  • Rusmevichientong P, Topaloglu H (2012) Robust assortment optimization in revenue management under the multinomial logit choice model. Oper. Res. 60(4):865–882.LinkGoogle Scholar
  • Rusmevichientong P, Shen ZJM, Shmoys D (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 PW (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.Google Scholar
  • Şimşek AS, Topaloglu H (2018) An expectation-maximization algorithm to estimate the parameters of the Markov chain choice model. Oper. Res. 66(3):748–760.LinkGoogle Scholar
  • Smith JE, Winkler RL (2006) The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Manage. Sci. 52(3):311–322.LinkGoogle Scholar
  • Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.Google Scholar
  • Talluri K, van Ryzin G (2006) The Theory and Practice of Revenue Management (Springer, Berlin).Google Scholar
  • Toubia O, Simester DI, Hauser JR, Dahan E (2003) Fast polyhedral adaptive conjoint estimation. Marketing Sci. 22(3):273–303.LinkGoogle Scholar
  • van Ryzin G, Vulcano G (2015) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.LinkGoogle Scholar
  • van Ryzin G, Vulcano G (2017) An expectation-maximization method to estimate a rank-based choice model of demand. Oper. Res. 65(2):396–407.LinkGoogle Scholar
  • Wiesemann W, Kuhn D, Rustem B (2013) Robust Markov decision processes. Math. Oper. Res. 38(1):153–183.LinkGoogle Scholar
  • Williams HC (1977) On the formation of travel demand models and economic evaluation measures of user benefit. Environment. Planning A 9(3):285–344.CrossrefGoogle Scholar
  • Xu H, Caramanis C, Mannor S (2009) Robust regression and Lasso. Koller D, Schuurmans D, Bengio Y, Bottou L, eds. Advances in Neural Information Processing Systems (Curran Associates Inc., Red Hook, NY), 1801–1808.Google Scholar
  • Zhang D, Cooper WL (2005) Revenue management for parallel flights with customer-choice behavior. Oper. Res. 53(3):415–431.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.