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Table 3 The results of queueing network for events A

From: Could we employ the queueing theory to improve efficiency during future mass causality incidents?

Queue Stationb Triage Assessment12a Assessment34a Treatment1 Treatment2 Consult
λ 41 5.002 35.998 7.99 33.008 41
μ 15 4 6 6 6 12
Servers 3 4 5 2 3 4 7 8 9 2 3 4 6 7 8 4 5 6
L s 11.310 3.603 2.945 2.053 1.362 1.269 9.681 7.070 6.391 2.393 1.476 1.358 14.124 7.179 6.055 7.501 4.176 3.632
L q 8.577 0.870 0.211 0.803 0.111 0.019 3.681 1.070 0.392 1.062 0.144 0.026 8.623 1.677 0.554 4.084 0.759 0.215
Ws (hour) 0.276 0.088 0.072 0.410 0.272 0.254 0.270 0.196 0.178 0.299 0.185 0.170 0.428 0.217 0.183 0.183 0.102 0.089
Wq (hour) 0.2091 0.021 0.005 0.160 0.022 0.004 0.102 0.029 0.011 0.133 0.018 0.003 0.261 0.051 0.017 0.100 0.019 0.005
ρ (%) 91.11 68.33 54.67 62.53 41.68 31.26 85.71 75.00 66.66 66.60 44.40 33.30 91.69 78.59 68.77 85.42 68.33 56.94
  1. aAssess12: assessment for triage 1 and 2. Assess34: assessment for triage 3 and 4
  2. bλ the arrival rate, µ the served rate, Ls Average number of people in the system, Lq Average length of the queue or the average number of people in a line awaiting service, Ws Average time for a customer in the system (waiting time plus service time), Wq Average waiting time or the average length of time that a customer waits before being served, ρ utilization factor for the system