Online Learning and Optimization of (Some) Cyclic Pricing Policies in the Presence of Patient Customers

Published Online:https://doi.org/10.1287/msom.2021.0979

References

  • Agrawal S, Jia R (2019) Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management. Karlin A, ed. Proc. 2019 ACM Conf. Econom. Comput. (ACM, New York), 743–744.CrossrefGoogle Scholar
  • Ahn H, Gümüş M, Kaminsky P (2007) Pricing and manufacturing decisions when demand is a function of prices in multiple periods. Oper. Res. 55(6):1039–1057.LinkGoogle Scholar
  • Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47(2–3):235–256.CrossrefGoogle Scholar
  • Aviv Y, Levin Y, Nediak M (2009) Counteracting strategic consumer behavior in dynamic pricing systems. Netessine S, Tang C, eds. Consumer-Driven Demand and Operations Management Models. International Series in Operations Research and Management Science, vol. 131 (Springer, New York), 323–352.Google Scholar
  • Besbes O, Lobel I (2015) Intertemporal price discrimination: Structure and computation of optimal policies. Management Sci. 61(1):92–110.LinkGoogle Scholar
  • Besbes O, Zeevi A (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.LinkGoogle Scholar
  • Besbes O, Zeevi A (2012) Blind network revenue management. Oper. Res. 60(6):1537–1550.LinkGoogle Scholar
  • Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.LinkGoogle Scholar
  • Caplin A, Dean M, Martin D (2011) Search and satisficing. Amer. Econom. Rev. 101(7):2899–2922.CrossrefGoogle Scholar
  • Chen Q, Jasin S, Duenyas I (2019a) A non-parametric self-adjusting control for joint learning and optimization of multi-product pricing with finite resource capacity. Math. Oper. Res. 44(2):601–631.LinkGoogle Scholar
  • Chen X, Wang Z (2016) Bayesian dynamic learning and pricing with strategic customers. Working paper, New York University, New York.Google Scholar
  • Chen Y, Farias VF (2018) Robust dynamic pricing with strategic customers. Math. Oper. Res. 43(4):1119–1142.LinkGoogle Scholar
  • Chen Y, Farias VF, Trichakis NK (2019b) On the efficacy of static prices for revenue management in the face of strategic customers. Management Sci. 65(12):5535–5555.LinkGoogle Scholar
  • Cheung WC, Simchi-Levi D, Wang H (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.LinkGoogle Scholar
  • Conlisk J, Gerstner E, Sobel J (1984) Cyclic pricing by a durable goods monopolist. Quart. J. Econom. 99(3):489–505.CrossrefGoogle Scholar
  • den Boer AV (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.CrossrefGoogle Scholar
  • den Boer AV, Keskin NB (2020) Discontinuous demand functions: Estimation and pricing. Management Sci. 66(10):4516–4534.Google Scholar
  • den Boer AV, Zwart B (2014) Simultaneously learning and optimizing using controlled variance pricing. Management Sci. 60(3):770–783.LinkGoogle Scholar
  • den Boer AV, Zwart B (2015) Dynamic pricing and learning with finite inventories. Oper. Res. 63(4):965–978.LinkGoogle Scholar
  • Ferreira K, Simchi-Levi D, Wang H (2018) Online network revenue management using Thompson sampling. Oper. Res. 66(6):1586–1602.LinkGoogle Scholar
  • Glazer A, Hassin R (1986) A deterministic single-item inventory model with seller holding cost and buyer holding and shortage costs. Oper. Res. 34(4):613–618.LinkGoogle Scholar
  • Harrison JM, Keskin NB, Zeevi A (2012) Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Sci. 58(3):570–586.LinkGoogle Scholar
  • Huh WT, Janakiraman G, Muckstadt JA, Rusmevichientong P (2009) An adaptive algorithm for finding the optimal base-stock policy in lost sales inventory systems with censored demand. Math. Oper. Res. 34(2):397–416.LinkGoogle Scholar
  • Kazerouni A, Van Roy B (2017) Learning to price with reference effects. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Keskin NB, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.LinkGoogle Scholar
  • Kopalle PK, Rao AG, Assuncao JL (1996) Asymmetric reference price effects and dynamic pricing policies. Marketing Sci. 15(1):60–85.LinkGoogle Scholar
  • Liu Y, Cooper WL (2015) Optimal dynamic pricing with patient customers. Oper. Res. 63(6):1307–1319.LinkGoogle Scholar
  • Lobel I (2020) Technical note—Dynamic pricing with heterogeneous patience levels. Oper. Res. 68(4):1038–1046.Google Scholar
  • Nasiry J, Popescu I (2011) Dynamic pricing with loss-averse consumers and peak-end anchoring. Oper. Res. 59(6):1361–1368.LinkGoogle Scholar
  • Osband I, Van Roy B (2014) Near-optimal reinforcement learning in factored MDPs. Adv. Neural Inform. Processing Systems 27:604–612.Google Scholar
  • Popescu I, Wu Y (2007) Dynamic pricing strategies with reference effects. Oper. Res. 55(3):413–429.LinkGoogle Scholar
  • Rejwan I, Mansour Y (2020) Top-k combinatorial bandits with full-bandit feedback. Kontorovich A, Neu G, eds. Proc. 31st Internat. Conf. Algorithmic Learning Theory, vol. 117 (PMLR, San Diego), 752–776.Google Scholar
  • Reutskaja E, Nagel R, Camerer CF, Rangel A (2011) Search dynamics in consumer choice under time pressure: An eye-tracking study. Amer. Econom. Rev. 101(2):900–926.CrossrefGoogle Scholar
  • Shen ZM, Su X (2007) Customer behavior modeling in revenue management and auctions: A review and new research opportunities. Production Oper. Management 16(6):713–728.CrossrefGoogle Scholar
  • Simon HA (1955) A behavioral model of rational choice. Quart. J. Econom. 69(1):99–118.CrossrefGoogle Scholar
  • Wang Z (2016) Intertemporal price discrimination via reference price effects. Oper. Res. 64(2):290–296.LinkGoogle Scholar
  • Wang Z, Deng S, Ye Y (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems. Oper. Res. 62(2):318–331.LinkGoogle Scholar
  • Zhang H, Chao X, Shi C (2020) Closing the gap: A learning algorithm for lost-sales inventory systems with lead times. Management Sci. 66(5):1962–1980.LinkGoogle Scholar
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