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Table 5 The best fitting results of queueing network for Events A and B

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

Event A B
Queue Stationb Triage Assess12a Assess34a X-ray CTa Tre1a Tre2a Consult Triage Assess12a Assess34a X-ray Tre1a Tre2a Consult
λ 41 5 35.99 31 9.01 7.99 33.01 41 22 16.85 5.15 8.89 4.68 17.32 22
μ 15 4 6 20 10 6 6 12 6 6 12 20 2 1 12
Servers 3 2 7 2 1 2 6 4 4 3 1 1 3 18 2
L s 11.31 2.05 9.68 3.88 9.15 2.39 14.12 7.50 12.71 15.75 0.75 0.80 4.52 38.25 11.48
L q 8.58 0.80 3.68 2.33 8.24 1.06 8.62 4.08 9.04 12.95 0.32 0.36 2.18 20.93 9.64
Ws (hour) 0.28 0.41 0.27 0.13 1.01 0.30 0.43 0.18 0.58 0.93 0.15 0.09 0.97 2.21 0.52
Wq (hour) 0.21 0.16 0.10 0.07 0.91 0.13 0.26 0.10 0.41 0.77 0.06 0.04 0.47 1.21 0.44
ρ (%) 91.11 62.53 85.71 77.50 90.14 66.60 91.69 85.42 91.67 93.62 42.90 44.47 77.79 96.22 91.67
  1. aAssess12: assessment for triage 1 and 2. Assess34: assessment for triage 3 and 4. CT computed tomography scan. Tre1: Treatment 1. Tre2: Treatment 2
  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