Feature-Driven Priority Queuing

Published Online:https://doi.org/10.1287/opre.2024.0754

References

  • Aksin Z, Ata B, Emadi SM, Su CL (2013) Structural estimation of callers’ delay sensitivity in call centers. Management Sci. 59(12):2727–2746.LinkGoogle Scholar
  • Argon NT, Ziya S (2009) Priority assignment under imperfect information on customer type identities. Manufacturing Service Oper. Management 11(4):674–693.LinkGoogle Scholar
  • Ata B, Ding Y, Zenios S (2021) An achievable-region-based approach for kidney allocation policy design with endogenous patient choice. Manufacturing Service Oper. Management 23(1):36–54.LinkGoogle Scholar
  • Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A (2019) Comparison of deep learning approaches for multi-label chest X-ray classification. Sci. Rep. 9(1):6381.CrossrefGoogle Scholar
  • Ban G, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.LinkGoogle Scholar
  • Beja A, Sid E (1975) Optimal priority assignment with heterogeneous waiting costs. Oper. Res. 23(1):107–117.LinkGoogle Scholar
  • Benjamens S, Dhunnoo P, Mesko B (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine 3(1):118.CrossrefGoogle Scholar
  • Bernardo J, Smith A (2009) Bayesian Theory (John Wiley & Sons, Chichester, UK).Google Scholar
  • Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.LinkGoogle Scholar
  • Bishop CM (2006) Pattern Recognition and Machine Learning (Springer, New York).Google Scholar
  • Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev. 60(2):223–311.CrossrefGoogle Scholar
  • Bren A, Saghafian S (2019) Data-driven percentile optimization for multiclass queueing systems with model ambiguity: Theory and application. INFORMS J. Optim. 1(4):267–287.LinkGoogle Scholar
  • Çelik S, Maglaras C (2008) Dynamic pricing and lead-time quotation for a multiclass make-to-order queue. Management Sci. 54(6):1132–1146.LinkGoogle Scholar
  • Cobham A (1954) Priority assignment in waiting line problems. J. Oper. Res. Soc. Amer. 2(1):70–76.LinkGoogle Scholar
  • Cohen A, Subramanian V, Zhang Y (2024) Learning-based optimal admission control in a single-server queuing system. Stochastics Systems 14(1):69–107.LinkGoogle Scholar
  • Cortes C, Mohri M (2003) AUC optimization vs. error rate minimization. Thrun S, Saul L, Schölkopf B, eds. Adv. Neural Inform. Processing Systems, vol. 16 (MIT Press, Cambridge, MA), 313–320.Google Scholar
  • Cox SDR, Smith WL (1961) Queues (John Wiley & Sons, New York).Google Scholar
  • Dai JG, Gluzman M (2022) Queueing network controls via deep reinforcement learning. Stochastic Systems 12(1):30–67.LinkGoogle Scholar
  • DeepTek AI (2017) Chest C-ray AI for hospitals and imaging centers. Accessed August 26, 2025, https://www.deeptek.ai/augmento-xray.Google Scholar
  • Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.LinkGoogle Scholar
  • Elmachtoub AN, Lam H, Lan H, Zhang H (2025) Dissecting the impact of model misspecification in data-driven optimization. Li Y, Mandt S, Agrawal S, Khan E, eds. Proc. 28th Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 258 (PMLR, New York), 1594–1602.Google Scholar
  • Folland GB (1999) Real Analysis: Modern Techniques and Their Applications, 2nd ed. (John Wiley & Sons, New York).Google Scholar
  • Garyfallos S, Biseda B, Khan M (2019) NIH-chest-X-rays-classification. Accessed August 26, 2025, https://github.com/paloukari/NIH-Chest-X-rays-Classification.Google Scholar
  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian Data Analysis, 3rd ed. (CRC Press, Boca Raton, FL).CrossrefGoogle Scholar
  • Goodfellow I, Bengio Y, Courville A (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
  • Greene WH (2018) Econometric Analysis, 8th ed. (Pearson, London).Google Scholar
  • Gurvich I, Lariviere MA, Ozkan C (2019) Coverage, coarseness, and classification: Determinants of social efficiency in priority queues. Management Sci. 65(3):1061–1075.LinkGoogle Scholar
  • Ibanez MR, Clark JR, Huckman RS, Staats BR (2018) Discretionary task ordering: Queue management in radiological services. Management Sci. 64(9):4389–4407.LinkGoogle Scholar
  • Jennrich RI (1969) Asymptotic properties of non-linear least squares estimators. Ann. Math. Statist. 40(2):633–643.CrossrefGoogle Scholar
  • Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.CrossrefGoogle Scholar
  • Lee J, Namkoong H, Zeng Y (2024) Design and scheduling of an AI-based queueing system. Preprint, submitted June 11, https://arxiv.org/abs/2406.06855.Google Scholar
  • Liyanage LH, Shanthikumar J (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.CrossrefGoogle Scholar
  • Maglaras C, Yao J, Zeevi A (2018) Optimal price and delay differentiation in large-scale queuing systems. Management Sci. 64(5):2427–2444.LinkGoogle Scholar
  • Millar R (2011) Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB (John Wiley & Sons, Chichester, UK).CrossrefGoogle Scholar
  • Murty KG (1983) Linear Programming (John Wiley & Sons, New York).Google Scholar
  • Nemirovski A (2009) Numerical methods for nonlinear continuous optimization. Lecture notes, Georgia Institute of Technology, Atlanta. Accessed May 18, 2026, https://www2.isye.gatech.edu/~nemirovs/Lect_OptII.pdf.Google Scholar
  • Qu G, Wierman A, Li N (2022) Scalable reinforcement learning for multiagent networked systems. Oper. Res. 70(6):3601–3628.LinkGoogle Scholar
  • Qure AI (2016) qXR: Chest X-ray interpretation using AI. Accessed August 26, 2025, https://www.qure.ai/product/qxr.Google Scholar
  • Ross SM (2010) Introduction to Probability Models, 10th ed. (Elsevier, Amsterdam).Google Scholar
  • Rudin W (1987) Real and Complex Analysis, 3rd ed. (McGraw-Hill, Columbus, OH).Google Scholar
  • See C, Sim M (2010) Robust approximation to multiperiod inventory management. Oper. Res. 58(3):583–594.LinkGoogle Scholar
  • Singh V, Cheng S, Kwan AC, Ebinger J (2025) United States Food and Drug Administration regulation of clinical software in the era of artificial intelligence and machine learning. Mayo Clinic Proc. Digital Health 3(3):100231.CrossrefGoogle Scholar
  • Sun Z, Argon NT, Ziya S (2022b) When to triage in service systems with hidden customer class identities? Production Oper. Management 31(1):172–193.CrossrefGoogle Scholar
  • Sun J, Zhang DJ, Hu H, Van Mieghem JA (2022a) Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations. Management Sci. 68(2):846–865.LinkGoogle Scholar
  • Tran TH, Nguyen LM, Scheinberg K (2022) Finding optimal policy for queueing models: New parameterization. Preprint, submitted June 21, https://arxiv.org/abs/2206.10073.Google Scholar
  • U.S. Department of Health and Human Services Radiation Emergency Medical Management (2025) Radiation triage. Accessed August 14, 2025, https://remm.hhs.gov/radtriage.htm.Google Scholar
  • U.S. Food and Drug Administration (2019) Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD): Discussion paper and request for feedback. Accessed May 18, 2026, https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf.Google Scholar
  • U.S. Food and Drug Administration (2025) Marketing submission recommendations for predetermined change control plans for artificial intelligence/machine learning (AI/ML)-enabled device software functions. Accessed May 18, 2026, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence.Google Scholar
  • van der Zee SP, Theil H (1961) Priority assignment in waiting-line problems under conditions of misclassification. Oper. Res. 9(6):875–885.LinkGoogle Scholar
  • Van Mieghem JA (1995) Dynamic scheduling with convex delay costs: The generalized cμ rule. Ann. Appl. Probab. 5(3):809–833.CrossrefGoogle Scholar
  • Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM (2017) ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proc. 2017 IEEE Conf. Comput. Vision Pattern Recognition (CVPR) (IEEE, Piscataway, NJ), 3462–3471.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.