Learning-Based Pricing and Matching for Two-Sided Queues

Published Online:https://doi.org/10.1287/stsy.2024.0073

References

  • Adan I, Weiss G (2012) Exact FCFS matching rates for two infinite multitype sequences. Oper. Res. 60(2):475–489.LinkGoogle Scholar
  • Agarwal A, Dekel O, Xiao L (2010) Optimal algorithms for online convex optimization with multi-point bandit feedback. Kalai AT, Mohri M, eds. Proc. 23rd Annual Conf. Learn. Theory (ACM, New York), 28–40.Google Scholar
  • Aveklouris A, Puha AL, Ward AR (2023) A fluid approximation for a matching model with general reneging distributions. Queueing Systems 106:199–238.Google Scholar
  • Aveklouris A, DeValve L, Stock M, Ward A (2024) Matching impatient and heterogeneous demand and supply. Oper. Res. 73(3):1637–1658.LinkGoogle Scholar
  • Besbes O, Zeevi A (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.LinkGoogle Scholar
  • Caldentey R, Kaplan EH, Weiss G (2009) FCFS infinite bipartite matching of servers and customers. Adv. Appl. Probability 41(3):695–730.Google Scholar
  • Chen Y, Hu M (2020) Pricing and matching with forward-looking buyers and sellers. Manufacturing Service Oper. Management 22(4):717–734.LinkGoogle Scholar
  • Chen B, Shi C (2024) Tailored base-surge policies in dual-sourcing inventory systems with demand learning. Oper. Res. 73(4):1723–1743.Google Scholar
  • Chen X, Liu Y, Hong G (2023) Online learning and optimization for queues with unknown demand curve and service distribution. Preprint, submitted March 6, https://arxiv.org/abs/2303.03399.Google Scholar
  • Chen X, Liu Y, Hong G (2024) An online learning approach to dynamic pricing and capacity sizing in service systems. Oper. Res. 72(6):2677–2697.LinkGoogle Scholar
  • Duchi JC, Jordan MI, Wainwright MJ, Wibisono A (2015) Optimal rates for zero-order convex optimization: The power of two function evaluations. IEEE Trans. Inform. Theory 61(5):2788–2806.Google Scholar
  • Flaxman AD, Kalai AT, McMahan HB (2004) Online convex optimization in the bandit setting: Gradient descent without a gradient. Preprint, submitted August 2, https://arxiv.org/abs/cs/0408007.Google Scholar
  • Gurvich I, Ward A (2015) On the dynamic control of matching queues. Stochastic Systems 4(2):479–523.LinkGoogle Scholar
  • Hu M, Zhou Y (2022) Dynamic type matching. Manufacturing Service Oper. Management 24(1):125–142.LinkGoogle Scholar
  • Lattimore T (2024) Bandit convex optimisation. Preprint, submitted February 9, https://arxiv.org/abs/2402.06535.Google Scholar
  • Lattimore T, Szepesvári C (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Google Scholar
  • Neely M (2022) Stochastic Network Optimization with Application to Communication and Queueing Systems (Springer, Cham, Switzerland).Google Scholar
  • Nguyen LM, Stolyar AL (2018) A queueing system with on-demand servers: Local stability of fluid limits. Queueing Systems 89:243–268.Google Scholar
  • Shamir O (2017) An optimal algorithm for bandit and zero-order convex optimization with two-point feedback. J. Machine Learn. Res. 18(52):1–11.Google Scholar
  • Slivkins A (2019) Introduction to multi-armed bandits. Foundations Trends Machine Learn. 12(1–2):1–286.Google Scholar
  • Srikant R, Ying L (2014) Communication Networks: An Optimization, Control and Stochastic Networks Perspective (Cambridge University Press, Cambridge, UK).Google Scholar
  • Tassiulas L, Ephremides A (1990) Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. Proc. 29th IEEE Conf. Decision Control (IEEE, Piscataway, NJ), 2130–2132.Google Scholar
  • Varma SM, Castro F, Maguluri ST (2020) Near optimal control in ride hailing platforms with strategic servers. Preprint, submitted August 9, https://arxiv.org/abs/2008.03762.Google Scholar
  • Varma SM, Bumpensanti P, Maguluri ST, Wang H (2023) Dynamic pricing and matching for two-sided queues. Oper. Res. 71(1):83–100.LinkGoogle Scholar
  • Vaze R, Nair J (2022) Non-asymptotic near optimal algorithms for two sided matchings. Proc. 20th Internat. Sympos. Modeling Optim. Mobile Ad Hoc Wireless Networks (IEEE, Piscataway, NJ), 17–24.Google Scholar
  • Yang Z, Srikant R, Ying L (2023) Learning while scheduling in multi-server systems with unknown statistics: Maxweight with discounted UCB. Ruiz F, Dy J, van de Meent J-W, eds. Proc. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 4275–4312.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.