Journal
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
Volume 16, Issue 2, Pages 283-299Publisher
INFORMS
DOI: 10.1287/msom.2013.0474
Keywords
healthcare; queueing networks; modified offered-load; time-varying queues; Halfin-Whitt regime; QED regime; ED regime; emergency department staffing; mass casualty events; patient flow
Funding
- United States-Israel Binational Science Foundation [2005175, 2008480]
- Israel Science Foundation [1357/08]
- Technion funds
- National Science Foundation
- Department of Statistics and Operations Research, the University of North Carolina at Chapel Hill
- Department of Information, Operations and Management Sciences, Leonard N. Stern School of Business, New York University
- Department of Statistics, the Wharton School, University of Pennsylvania
- Israel National Institute for Health Policy and Health Services Research
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We analyze a queueing model that we call Erlang-R, where the R stands for reentrant customers. Erlang-R accommodates customers who return to service several times during their sojourn within the system, and its modeling power is most pronounced in time-varying environments. Indeed, it was motivated by healthcare systems, in which offered-loads vary over time and patients often go through a repetitive service process. Erlang-R helps answer questions such as how many servers (physicians/nurses) are required to achieve predetermined service levels. Formally, it is merely a two-station open queueing network, which, in a steady state, evolves like an Erlang-C (M/M/s) model. In time-varying environments, on the other hand, the situation differs: here one must account for the reentrant nature of service to avoid excessive staffing costs or undesirable service levels. We validate Erlang-R against an emergency ward (EW) operating under normal conditions as well as during a mass casualty event (MCE). In both scenarios, we apply time-varying fluid and diffusion approximations: the EW is critically loaded and the MCE is overloaded. In particular, for the EW we propose a time-varying square-root staffing policy, based on the modified offered-load, which is proved to perform well over small-to-large systems.
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