On Greedy-Like Policies in Online Matching with Reusable Network Resources and Decaying Rewards

Published Online:https://doi.org/10.1287/mnsc.2023.02588

References

  • Adelman D (2007) Dynamic bid prices in revenue management. Oper. Res. 55(4):647–661.LinkGoogle Scholar
  • Aggarwal G, Goel G, Karande C, Mehta A (2011) Online vertex-weighted bipartite matching and single-bid budgeted allocations. Proc. 22nd Annual ACM-SIAM Sympos. on Discrete Algorithms (SIAM, Philadelphia), 1253–1264.Google Scholar
  • Atsidakou A, Papadigenopoulos O, Basu S, Caramanis C, Shakkottai S (2021) Combinatorial blocking bandits with stochastic delays. Internat. Conf. Machine Learn. (PMLR, New York), 404–413.Google Scholar
  • Baek J, Ma W (2022) Bifurcating constraints to improve approximation ratios for network revenue management with reusable resources. Oper. Res. 70(4):2226–2236.Google Scholar
  • Ball MO, Queyranne M (2009) Toward robust revenue management: Competitive analysis of online booking. Oper. Res. 57(4):950–963.LinkGoogle Scholar
  • Balseiro SR, Besbes O, Pizarro D (2024) Survey of dynamic resource-constrained reward collection problems: Unified model and analysis. Oper. Res. 72(5):2168–2189.Google Scholar
  • Banerjee S, Freund D (2020) SIGMETRICS’20. Abstracts of the 2020 SIGMETRICS/Performance Joint Internat. Conf. Measurement and Modeling of Comput. Systems, 1–2.Google Scholar
  • Basu S, Papadigenopoulos O, Caramanis C, Shakkottai S (2020) Contextual blocking bandits. arXiv preprint arXiv:2003.03426.Google Scholar
  • Basu S, Sen R, Sanghavi S, Shakkottai S (2019) Blocking bandits. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Inc., Red Hook, NY), 4784–4793.Google Scholar
  • Bertsimas D, Popescu I (2003) Revenue management in a dynamic network environment. Transportation Sci. 37(3):257–277.LinkGoogle Scholar
  • Borodin A, Karavasilis C, Pankratov D (2020) An experimental study of algorithms for online bipartite matching. ACM J. Experiment. Algorithmics 25:1–37.CrossrefGoogle 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
  • Cella L, Cesa-Bianchi N (2020) Stochastic bandits with delay-dependent payoffs. Proc. Internat. Conf. Artificial Intelligence and Statist. (PMLR, New York), 1168–1177.Google 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 W, Wang Y, Yuan Y (2013) Combinatorial multi-armed bandit: General framework and applications. Proc. Internat. Conf. Machine Learn. (PMLR), 151–159.Google Scholar
  • Dai J, Kleywegt AJ, Xiao Y (2019) Network revenue management with cancellations and no-shows. Production Oper. Management 28(2):292–318.CrossrefGoogle Scholar
  • Delong S, Farhadi A, Niazadeh R, Sivan B (2022) Online bipartite matching with reusable resources. Proc. 23rd ACM Conf. Econom. Comput. (ACM, New York), 962–963.Google Scholar
  • Dickerson J, Sankararaman K, Srinivasan A, Xu P (2021) Allocation problems in ride-sharing platforms: Online matching with offline reusable resources. ACM Trans. Econom. Comput. (TEAC) 9(3):1–17.Google Scholar
  • Feng Y, Niazadeh R, Saberi A (2019) Linear programming based online policies for real-time assortment of reusable resources. Chicago Booth Res. Paper, 20–25.Google Scholar
  • Feng Y, Niazadeh R, Saberi A (2022) Near-optimal Bayesian online assortment of reusable resources. Proc. 23rd ACM Conf. Econom. Comput. (ACM, New York), 964–965.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 GN, 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://doi.org/10.1287/opre.2021.0695.Google Scholar
  • Hazan E, Safra S, Schwartz O (2006) On the complexity of approximating k-set packing. Comput. Complex 15(1):20–39.CrossrefGoogle Scholar
  • Heidari H, Kearns MJ, Roth A (2016) Tight policy regret bounds for improving and decaying bandits. Proc. Twenty-Fifth Internat. Joint Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 1562–1570.Google Scholar
  • Hu M, Zhou Y (2022) Dynamic type matching. Manufacturing Service Oper. Management 24(1):125–142.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
  • Jasin S, Kumar S (2013) Analysis of deterministic lp-based booking limit and bid price controls for revenue management. Oper. Res. 61(6):1312–1320.LinkGoogle Scholar
  • Karp RM, Vazirani UV, Vazirani VV (1990) An optimal algorithm for on-line bipartite matching. Proc. 22nd Annual ACM Sympos. Theory Comput. (ACM, New York), 352–358.Google Scholar
  • Kleinberg R, Immorlica N (2018) Recharging bandits. Proc. IEEE 59th Annual Sympos. Foundations Comput. Sci. (IEEE, New York), 309–319.Google Scholar
  • Kunnumkal S, Topaloglu H (2010) Computing time-dependent bid prices in network revenue management problems. Transportation Sci. 44(1):38–62.LinkGoogle Scholar
  • Lei Y, Jasin S (2020) Real-time dynamic pricing for revenue management with reusable resources, advance reservation, and deterministic service time requirements. Oper. Res. 68(3):676–685.LinkGoogle Scholar
  • Levi R, Radovanović A (2010) Provably near-optimal LP-based policies for revenue management in systems with reusable resources. Oper. Res. 58(2):503–507.LinkGoogle Scholar
  • Levine N, Crammer K, Mannor S (2017) Rotting bandits. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY).Google Scholar
  • Ma W, Simchi-Levi D (2020) Algorithms for online matching, assortment, and pricing with tight weight-dependent competitive ratios. Oper. Res. 68(6):1787–1803.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
  • Mehta A (2013) Online matching and ad allocation. Foundations Trends Theoretical Comput. Sci. 8(4):265–368.CrossrefGoogle Scholar
  • Owen Z, Simchi-Levi D (2018) Price and assortment optimization for reusable resources. Preprint, submitted February 19, https://dx.doi.org/10.2139/ssrn.3070625.Google Scholar
  • Papadigenopoulos O, Caramanis C (2021) Recurrent submodular welfare and matroid blocking semi-bandits. Adv. Neural Inform. Processing Systems 34:23334–23346.Google Scholar
  • Pike-Burke C, Grunewalder S (2019) Recovering bandits. Wallach H, Larochelle H, Beygelzimer A, d’Alche-Buc, Fox E, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Inc., Red Hook, NY), 14122–14131.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, Sumida M, Topaloglu H (2020) Dynamic assortment optimization for reusable products with random usage durations. Management Sci. 66(7):2820–2844.LinkGoogle Scholar
  • Rusmevichientong P, Sumida M, Topaloglu H, Bai Y (2023) Revenue management with heterogenous resources: Unit resource capacities, advance bookings, and itineraries over time intervals. Oper. Res. 71(6):2196–2216.Google Scholar
  • Seznec J, Locatelli A, Carpentier A, Lazaric A, Valko M (2023) Rotting bandits are no harder than stochastic ones. Proc. 22nd Internat. Conf. Artificial Intelligence and Statist. (PMLR, New York), 2564–2572.Google Scholar
  • Seznec J, Menard P, Lazaric A, Valko M (2020) A single algorithm for both restless and rested rotting bandits. Proc. Internat. Conf. Artificial Intelligence and Statist. (PMLR, New York), 3784–3794.Google Scholar
  • Simchi-Levi D, Zheng Z, Zhu F (2021) Dynamic planning and learning under recovering rewards. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 9702–9711.Google Scholar
  • Topaloglu H (2009) Using lagrangian relaxation to compute capacity-dependent bid prices in network revenue management. Oper. Res. 57(3):637–649.LinkGoogle Scholar
  • Udwani R (2022) Periodic reranking for online matching of reusable resources. Proc. 23rd ACM Conf. Econom. Comput., 966.Google Scholar
  • Vera A, Banerjee S (2021) The bayesian prophet: A low-regret framework for online decision making. Management Sci. 67(3):1368–1391.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
  • Zhu F, Liu S, Wang R, Wang Z (2023) Assign-to-seat: Dynamic capacity control for selling high-speed train tickets. Manufacturing Service Oper. Management 25(3):921–938.Google 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.