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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