Service-level computation in time-varying queueing system with priorities: Application to physician staffing in the emergency department

References

• Arora, S., Taylor, J.W. and Mak, H.Y. (2023) Probabilistic forecasting of patient waiting times in an emergency department. Manufacturing & Service Operations Management, 25(4), 1489–1508.
   Google Scholar
• Atlason, J., Epelman, M.A. and Henderson, S.G. (2004) Call center staffing with simulation and cutting plane methods. Annals of Operations Research, 127, 333–358.
   Web of Science ®Google Scholar
• Atlason, J., Epelman, M.A. and Henderson, S.G. (2008) Optimizing call center staffing using simulation and analytic center cutting-plane methods. Management Science, 54(2), 295–309.
   Web of Science ®Google Scholar
• Defraeye, M. and Van Nieuwenhuyse, I. (2013) Controlling excessive waiting times in small service systems with time-varying demand: An extension of the ISA algorithm. Decision Support Systems, 54(4), 1558–1567.
   Web of Science ®Google Scholar
• Ding, Y., Park, E., Nagarajan, M. and Grafstein, E. (2019) Patient prioritization in emergency department triage systems: An empirical study of the Canadian triage and acuity scale (CTAS). Manufacturing & Service Operations Management, 21(4), 723–741.
   Google Scholar
• Dobson, G., Lee, H.H., Sainathan, A. and Tilson, V. (2012) A queueing model to evaluate the impact of patient “batching” on throughput and flow time in a medical teaching facility. Manufacturing & Service Operations Management, 14(4), 584–599.
   Google Scholar
• Feldman, Z., Mandelbaum, A., Massey, W.A. and Whitt, W. (2008) Staffing of time-varying queues to achieve time-stable performance. Management Science, 54(2), 324–338.
   Web of Science ®Google Scholar
• Franx, G.J., Koole, G. and Pot, A. (2006) Approximating multi-skill blocking systems by hyperexponential decomposition. Performance Evaluation, 63(8), 799–824.
   Web of Science ®Google Scholar
• Fügener, A. and Brunner, J.O. (2019) Planning for overtime: The value of shift extensions in physician scheduling. INFORMS Journal on Computing, 31(4), 732–744.
   Web of Science ®Google Scholar
• Gans, N. and Zhou, Y.P. (2003) A call-routing problem with service level constraints. Operations Research, 51(2), 255–271.
   Web of Science ®Google Scholar
• Green, L.V. and Soares, J. (2007) Note-computing time-dependent waiting time probabilities in M(t)/M/s(t) queuing systems. Manufacturing & Service Operations Management, 9(1), 54–61.
   Google Scholar
• He, S., Sim, M. and Zhang, M. (2019) Data-driven patient scheduling in emergency departments: A hybrid robust-stochastic approach. Management Science, 65(9), 4123–4140.
   Web of Science ®Google Scholar
• Huang, J., Carmeli, B. and Mandelbaum, A. (2015) Control of patient flow in emergency departments, or multiclass queues with deadlines and feedback. Operations Research, 63(4), 892–908.
   Web of Science ®Google Scholar
• Ingolfsson, A., Akhmetshina, E., Budge, S., Li, Y. and Wu, X. (2007) A survey and experimental comparison of service level-approximation methods for nonstationary M(t)/M/s(t) queueing systems with exhaustive discipline. INFORMS Journal on Computing, 19(2), 201–214.
   Web of Science ®Google Scholar
• Ingolfsson, A., Campello, F., Wu, X. and Cabral, E. (2010) Combining integer programming and the randomization method to schedule employees. European Journal of Operational Research, 202(1), 153–163.
   Web of Science ®Google Scholar
• Izady, N. and Worthington, D. (2012) Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments. European Journal of Operational Research, 219(3), 531–540.
   Web of Science ®Google Scholar
• Jennings, O.B., Mandelbaum, A., Massey, W.A. and Whitt, W. (1996) Server staffing to meet time-varying demand. Management Science, 42(10), 1383–1394.
   Web of Science ®Google Scholar
• Ko, Y.M. and Gautam, N. (2013) Critically loaded time-varying multiserver queues: Computational challenges and approximations. INFORMS Journal on Computing, 25(2), 285–301.
   Web of Science ®Google Scholar
• Koole, G. and Van Der Sluis, E. (2003) Optimal shift scheduling with a global service level constraint. IIE Transactions, 35(11), 1049–1055.
   Web of Science ®Google Scholar
• Li, W., Sun, Z. and Hong, L.J. (2023) Who is next: Patient prioritization under emergency department blocking. Operations Research, 71(3), 821–842.
   Web of Science ®Google Scholar
• Liu, Y. (2018) Staffing to stabilize the tail probability of delay in service systems with time-varying demand. Operations Research, 66(2), 514–534.
   Web of Science ®Google Scholar
• Liu, Y. and Whitt, W. (2012) Stabilizing customer abandonment in many-server queues with time-varying arrivals. Operations Research, 60(6), 1551–1564.
   Web of Science ®Google Scholar
• Mandelbaum, A., Massey, W.A. and Reiman, M.I. (1998) Strong approximations for Markovian service networks. Queueing Systems, 30, 149–201.
   Web of Science ®Google Scholar
• Pan, X., Geng, N., Xie, X. and Wen, J. (2020) Managing appointments with waiting time targets and random walk-ins. Omega, 95, 102062.
   Web of Science ®Google Scholar
• Pender, J. (2017) Sampling the functional Kolmogorov forward equations for nonstationary queueing networks. INFORMS Journal on Computing, 29(1), 1–17.
   Web of Science ®Google Scholar
• Pot, A., Bhulai, S. and Koole, G. (2008) A simple staffing method for multiskill call centers. Manufacturing & Service Operations Management, 10(3), 421–428.
   Web of Science ®Google Scholar
• Puterman, M.L. (2014) Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, New Jersey.
   Google Scholar
• Roubos, A., Koole, G. and Stolletz, R. (2012) Service-level variability of inbound call centers. Manufacturing & Service Operations Management, 14(3), 402–413.
   Google Scholar
• Saghafian, S., Austin, G. and Traub, S.J. (2015) Operations research/management contributions to emergency department patient flow optimization: Review and research prospects. IIE Transactions on Healthcare Systems Engineering, 5(2), 101–123.
   Google Scholar
• Saghafian, S., Hopp, W.J., Iravani, S.M., Cheng, Y. and Diermeier, D. (2018) Workload management in telemedical physician triage and other knowledge-based service systems. Management Science, 64(11), 5180–5197.
   Web of Science ®Google Scholar
• Saghafian, S., Hopp, W.J., Van Oyen, M.P., Desmond, J.S. and Kronick, S.L. (2012) Patient streaming as a mechanism for improving responsiveness in emergency departments. Operations Research, 60(5), 1080–1097.
   Web of Science ®Google Scholar
• Saghafian, S., Hopp, W.J., Van Oyen, M.P., Desmond, J.S. and Kronick, S.L. (2014) Complexity-augmented triage: A tool for improving patient safety and operational efficiency. Manufacturing & Service Operations Management, 16(3), 329–345.
   Web of Science ®Google Scholar
• Traub, S.J., Bartley, A.C., Smith, V.D., Didehban, R., Lipinski, C.A. and Saghafian, S. (2016) Physician in triage versus rotational patient assignment. The Journal of Emergency Medicine, 50(5), 784–790.
   PubMed Web of Science ®Google Scholar
• Traub, S.J., Butler, R., Chang, Y.H. and Lipinski, C. (2013). Emergency department physician telemedical triage. Telemedicine and e-Health, 19(11), 841–845.
   PubMed Web of Science ®Google Scholar
• Zaerpour, F., Bijvank, M., Ouyang, H. and Sun, Z. (2022) Scheduling of physicians with time-varying productivity levels in emergency departments. Production and Operations Management, 31(2), 645–667.
   Web of Science ®Google Scholar