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Table 4 The results of queueing network for events B

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

Queue Stationb Triage Assessment12a Assessment34a Treatment1 Treatment2 Consult
λ 22 16.852 5.148 4.6795 17.3205 22
µ 6 6 12 2 1 12
Servers 4 5 6 3 4 5 1 2 3 3 4 5 18 19 20 2 3 4
Ls 12.706 4.857 3.997 15.755 3.827 3.054 0.751 0.449 0.431 4.519 2.718 2.431 38.249 23.545 20.126 11.478 2.413 1.948
Lq 9.039 1.190 0.330 12.946 1.018 0.245 0.322 0.021 0.002 2.179 0.378 0.091 20.928 6.225 2.805 9.645 0.580 0.115
Ws (hour) 0.578 0.221 0.182 0.935 0.227 0.181 0.146 0.087 0.084 0.966 0.581 0.519 2.208 1.359 1.162 0.522 0.109 0.088
Wq (hour) 0.411 0.054 0.015 0.768 0.060 0.015 0.063 0.004 0.0003 0.466 0.081 0.019 1.208 0.359 0.162 0.438 0.026 0.005
ρ (%) 91.67 73.33 61.11 93.62 70.22 56.17 42.90 21.45 14.30 77.99 58.49 46.80 96.22 91.16 86.60 91.67 61.11 45.83
  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