Overbooking with Bounded Loss

References

• [1] Agrawal S, Devanur NR (2014) Fast algorithms for online stochastic convex programming. Indyk P, ed. Proc. 26th Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 1405–1424.Google Scholar
• [2] Agrawal S, Devanur NR, Li L (2016) An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. Feldman V, Rakhlin A, Shamir O, eds. Conf. Learn. Theory (PMLR, New York), 4–18.Google Scholar
• [3] Alaei S, Hajiaghayi MT, Liaghat V (2012) Online prophet-inequality matching with applications to ad allocation. Faltings B, Leyton-Brown K, Ipeirotis P, eds. Proc. 13th ACM Conf. Electronic Commerce (Association for Computing Machinery, New York), 18–35.Google Scholar
• [4] Alaei S, Hajiaghayi MT, Liaghat V (2013) The online stochastic generalized assignment problem. Approximation, Randomization, and Combinatorial Optimization: Algorithms and Techniques (Springer, New York), 11–25.Crossref, Google Scholar
• [5] Arlotto A, Gurvich I (2019) Uniformly bounded regret in the multisecretary problem. Stochastic Systems 9(3):231–260.Link, Google Scholar
• [6] Arlotto A, Xie X (2020) Logarithmic regret in the dynamic and stochastic knapsack problem with equal rewards. Stochastic Systems 10(2):170–191.Link, Google Scholar
• [7] Aydin N, İlker Birbil Ş, Frenk JBG, Noyan N (2013) Single-leg airline revenue management with overbooking. Transportation Sci. 47(4):560–583.Link, Google Scholar
• [8] Beckmann MJ (1958) Decision and team problems in airline reservations. Econometrica 26(1):134–145.Crossref, Google Scholar
• [9] Belobaba P (1987) Air travel demand and airline seat inventory management. Unpublished PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
• [10] Bitran GR, Gilbert SM (1996) Managing hotel reservations with uncertain arrivals. Oper. Res. 44(1):35–49.Link, Google Scholar
• [11] Bitran GR, Mondschein SV (1995) An application of yield management to the hotel industry considering multiple day stays. Oper. Res. 43(3):427–443.Link, Google Scholar
• [12] Bray RL (2019) Logarithmic regret in multisecretary and online linear programming problems with continuous valuation. Preprint, submitted December 16, https://arxiv.org/abs/1912.08917.Google Scholar
• [13] Bumpensanti P, Wang H (2020) A re-solving heuristic with uniformly bounded loss for network revenue management. Management Sci. 66(7):2993–3009.Google Scholar
• [14] Chen G, Li X, Ye Y (2022) An improved analysis of LP-based control for revenue management. Oper. Res. Forthcoming.Google Scholar
• [15] Cooper WL (2002) Asymptotic behavior of an allocation policy for revenue management. Oper. Res. 50(4):720–727.Link, Google Scholar
• [16] Dai J, Kleywegt AJ, Xiao Y (2019) Network revenue management with cancellations and no-shows. Production Oper. Management 28(2):292–318.Crossref, Google Scholar
• [17] Erdelyi A, Topaloglu H (2010) A dynamic programming decomposition method for making overbooking decisions over an airline network. INFORMS J. Comput. 22(3):443–456.Link, Google Scholar
• [18] Banerjee S, Freund D (2019) Good prophets know when the end is near. Preprint, submitted November 25, https://dx.doi.org/10.2139/ssrn.3479189.Google Scholar
• [19] Gallego G, Topaloglu H (2019) Overbooking. Revenue Management and Pricing Analytics (Springer, New York), 83–105.Crossref, Google Scholar
• [20] Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics, vol. 209 (Springer, New York).Crossref, Google Scholar
• [21] Geraghty MK, Johnson E (1997) Revenue management saves national car rental. Interfaces 27(1):107–127.Link, Google Scholar
• [22] Gupta V, Radovanović A (2020) Interior-point-based online stochastic bin packing. Oper. Res. 68(5):1474–1492.Link, Google Scholar
• [23] 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.Link, Google Scholar
• [24] Kunnumkal S, Talluri K, Topaloglu H (2012) A randomized linear programming method for network revenue management with product-specific no-shows. Transportation Sci. 46(1):90–108.Link, Google Scholar
• [25] Lautenbacher CJ, Stidham S Jr (1999) The underlying Markov decision process in the single-leg airline yield-management problem. Transportation Sci. 33(2):136–146.Link, Google Scholar
• [26] Littlewood K (1972) Forecasting and control of passenger bookings. Airline Group Internat. Federation Oper. Res. Soc. Proc., vol. 12, 95–117.Google Scholar
• [27] Mangasarian OL, Shiau TH (1987) Lipschitz continuity of solutions of linear inequalities, programs and complementarity problems. SIAM J. Control Optim. 25(3):583–595.Crossref, Google Scholar
• [28] McGill JI, van Ryzin GJ (1999) Revenue management: Research overview and prospects. Transportation Sci. 33(2):233–256.Link, Google Scholar
• [29] Metters R, Vargas V (1999) Yield management for the nonprofit sector. J. Service Res. 1(3):215–226.Crossref, Google Scholar
• [30] OpenTable (2021) OpenTable support. Accessed February 2, 2021, https://support.opentable.com/s/article/Passcode-Protection-for-Key-Features-1505261473577?language=en_US.Google Scholar
• [31] Reiman MI, Wang Q (2008) An asymptotically optimal policy for a quantity-based network revenue management problem. Math. Oper. Res. 33(2):257–282.Link, Google Scholar
• [32] Rothstein M (1971) An airline overbooking model. Transportation Sci. 5(2):180–192.Link, Google Scholar
• [33] Sun R, Wang X, Zhou Z (2020) Near-optimal primal-dual algorithms for quantity-based network revenue management. Preprint, submitted November 12, https://arxiv.org/abs/2011.06327.Google Scholar
• [34] Talluri K, van Ryzin G (1999) A randomized linear programming method for computing network bid prices. Transportation Sci. 33(2):207–216.Link, Google Scholar
• [35] Talluri KT, van Ryzin GJ (2006) The Theory and Practice of Revenue Management, vol. 68 (Springer Science & Business Media, Boston).Google Scholar
• [36] Thompson HR (1961) Statistical problems in airline reservation control. J. Oper. Res. Soc. 12(3):167–185.Crossref, Google Scholar
• [37] Vera A, Banerjee S (2021) The Bayesian prophet: A low-regret framework for online decision making. Management Sci. 67(3):1368–1391.Link, Google Scholar
• [38] Vera A, Banerjee S, Gurvich I (2019) Online allocation and pricing: Constant regret via bellman inequalities. Preprint, submitted June 14, https://arxiv.org/abs/1906.06361.Google Scholar