Performance of the Offer-Everything Policy

Published Online:https://doi.org/10.1287/opre.2021.0417

References

  • Ahuja R, Magnanti T, Orlin J (1993) Network Flows: Theory, Algorithms, and Applications (Prentice-Hall, Inc., Division of Simon and Schuster, Upper Saddle River, NJ).Google Scholar
  • Albers S, Schubert S (2021) Optimal algorithms for online b-matching with variable vertex capacities. Wootters M, Sanità L, eds. Approximation Randomization Combin. Optimiz. Algorithms Techniques (APPROX/RANDOM 2021), 2:1–2:18.Google Scholar
  • Aouad ALI, Segev D (2022) The stability of MNL-based demand under dynamic customer substitution and its algorithmic implications. Oper. Res. 71(4):1216–1249. LinkGoogle Scholar
  • 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, Levi R, Segev D (2018b) Greedy-like algorithms for dynamic assortment planning under multinomial logit preferences. Oper. Res. 66(5):1321–1345.LinkGoogle Scholar
  • Aouad A, Levi R, Segev D (2019) Approximation algorithms for dynamic assortment optimization models. Math. Oper. Res. 44(2):487–511.LinkGoogle Scholar
  • Aouad A, Farias V, Levi R, Segev D (2018a) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.LinkGoogle Scholar
  • Ball MO, Queyranne M (2009) Toward robust revenue management: Competitive analysis of online booking. Oper. Res. 57(4):905–963.LinkGoogle Scholar
  • Berbeglia G, Joret G (2020) Assortment optimisation under a general discrete choice model: A tight analysis of revenue-ordered assortments. Algorithmica 82(4):681–720.CrossrefGoogle Scholar
  • Bernstein F, Kök AG, Xie L (2015) Dynamic assortment customization with limited inventories. Manufacturing Service Oper. Management 17(4):538–553.LinkGoogle Scholar
  • Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905. LinkGoogle Scholar
  • Chan CW, Farias VF (2009) Stochastic depletion problems: Effective myopic policies for a class of dynamic optimization problems. Math. Oper. Res. 34(2):333–350.LinkGoogle Scholar
  • Chen L, Homem-de-Mello T (2010) Re-solving stochastic programming models for airline revenue management. Ann. Oper. Res. 177:91–114.CrossrefGoogle Scholar
  • Chen X, Ma W, Simchi-Levi D, Xin L (2023) Assortment planning for recommendations at checkout under inventory constraints. Math. Oper. Res. 49(1):297–325.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
  • Feng Y, Niazadeh R, Saberi A (2021) Near-optimal Bayesian online assortment of reusable resources. Proc. 23rd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 964–965.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, Ratliff R, Shebalov S (2015) A general attraction model and sales-based linear program 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. Working paper, The Chinese University of Hong Kong Shenzhen, Shenzhen, China.Google Scholar
  • Golrezaei N, Nazerzadeh H, Rusmevichientong P (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.LinkGoogle Scholar
  • Gong XY, Goyal V, Iyengar G, Simchi-Levi D, Udwani R, Wang S (2022) Online assortment optimization with reusable resources. Management Sci. 68(7):4772–4785.LinkGoogle Scholar
  • Goyal V, Iyengar G, Udwani R (2025) Asymptotically optimal competitive ratio for online allocation of reusable resources. Oper. Res., ePub ahead of print January 21, https://dx.doi.org/10.1287/opre.2021.0695.Google Scholar
  • Goyal V, Iyengar G, Udwani R (2020) Online allocation of reusable resources via algorithms guided by fluid approximations. Working paper, Columbia University, New York.Google Scholar
  • Goyal V, Levi R, Segev D (2016) Near-optimal algorithms for the assortment planning problem under dynamic substitution and stochastic demand. Oper. Res. 64(1):219–235.LinkGoogle Scholar
  • Honhon D, Seshadri S (2013) Fixed vs. random proportions demand models for the assortment planning problem under stockout-based substitution. Manufacturing Service Oper. Management 15(3):378–386.LinkGoogle Scholar
  • Honhon D, Gaur V, Seshadri S (2010) Assortment planning and inventory decisions under stockout-based substitution. Oper. Res. 58(5):1364–1379.LinkGoogle Scholar
  • Irving R (1994) Stable marriage and indifference. Discrete Appl. Math. 48:261–272.CrossrefGoogle 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
  • Kalyanasundaram B, Pruhs K (2000) An optimal deterministic algorithm for online b-matching. Theoretical Comput. Sci. 233(1–2):319–325.CrossrefGoogle Scholar
  • Karp R, Vazirani U, Vazirani V (1990) An optimal algorithm for on-line bipartite matching. Proc. 22nd Ann. ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 352–358.Google Scholar
  • Kuhller S, Mitchell S, Vazirani V (1994) On-line algorithms for weighted bipartite matching and stable marriages. Theoretical Comput. Sci. 2:255–267.CrossrefGoogle Scholar
  • Kunnumkal S, Talluri K (2016a) Note on relaxations of the choice network revenue management dynamic program. Oper. Res. 64(1):158–166.LinkGoogle Scholar
  • Kunnumkal S, Talluri K (2016b) On a piecewise-linear approximation for network revenue management. Math. Oper. Res. 41(1):72–91.LinkGoogle Scholar
  • Kunnumkal S, Topaloglu H (2008) A refined deterministic linear program for the network revenue management problem with customer choice behavior. Naval Res. Logist. 55(6):563–580.CrossrefGoogle Scholar
  • Kunnumkal S, Topaloglu H (2010) A new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. Production Oper. Management 19(5):575–590.CrossrefGoogle Scholar
  • Lan Y, Gao H, Ball MO, Karaesman I (2008) Revenue management with limited demand information. Management Sci. 54(90):1594–1609.LinkGoogle Scholar
  • Liang J, Jasin S, Uichanco J (2020) Assortment and inventory planning under dynamic substitution with MNL model: Structural results and near-optimal heuristic. Working paper, University of Michigan, Ann Arbor.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.LinkGoogle Scholar
  • Ma W, Simchi-Levi D, Teo CP (2021) On policies for single-leg revenue management with limited demand information. Oper. Res. 69(1):207–226.LinkGoogle Scholar
  • Ma Y, Rusmevichientong P, Sumida M, Topaloglu H (2020) An approximation algorithm for network revenue management under nonstationary arrivals. Oper. Res. 68(3):834–855.LinkGoogle Scholar
  • Miranda Bront JJ, Méndez-Díaz I, Vulcano G (2009) A column generation algorithm for choice-based network revenue management. Oper. Res. 57(3):769–784.LinkGoogle Scholar
  • Rusmevichientong P, Sumida M, Topaloglu H (2020) Dynamic assortment optimization for reusable products with random usage durations. Management Sci. 66(7):2840–2844.Google Scholar
  • Segev D (2019) Assortment planning with nested preferences: Dynamic programming with distributions as states? Algorithmica 81(1):393–417.CrossrefGoogle Scholar
  • Talluri K, Van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33. LinkGoogle Scholar
  • Vossen TWM, Zhang D (2015) Reductions of approximate linear programs for network revenue management. Oper. Res. 63(6):1352–1371.LinkGoogle Scholar
  • Zhang D, Adelman D (2009) An approximate dynamic programming approach to network revenue management with customer choice. Transportation Sci. 43(3):381–394.LinkGoogle 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.