PDF-6.4MB - Faculty of Industrial Engineering and Management

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31 Queue Empirical Fluid Model: Queue-Length at a Bank Queue Catastrophic/Heavy/Regular Day(s) 60 50 40 30 20 10 Bank Queue 0 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 Time of Day Catastrophic Heavy Load Regular
Daily Queues Israeli Call Center, November 1999 number of calls number of calls number of calls 14 12 10 14 12 10 8 6 4 2 0 14 12 10 8 6 4 2 0 8 6 4 2 0 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 20 16:00 time 16:00 time 16:00 time queue queue queue 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00
Page 1 and 2: Service Engineering Class 5 Fluid/F
Page 3 and 4: Types of Queues • Perpetual Queue
Page 5 and 6: The “Fluid View” or Flow Models
Page 7 and 8: Contents • Scenario Analysis: Emp
Page 9: Shouldice Hospital: Flow Chart of P
Page 13 and 14: Number of cases 9000 8000 7000 6000
Page 15 and 16: Optimizing Patient Flow by Managing
Page 17 and 18: Patients per Hour Average Patients
Page 27 and 28: Optimizing Patient Flow by Managing
Page 29 and 30: ✬ ✫ Service Times: The Human Fa
Page 31: Scales (Fig. 2.1 in Newell’s book
Page 34 and 35: 41 Arrivals Process, in 1976 Arriva
Page 38 and 39: Fluid Models and Empirical Models S
Page 43 and 44: This suggests that our empirical mo
Page 45 and 46: Predictable Queues Fluid Models and
Page 47 and 48: Time-Varying Queues with Abandonmen
Page 49 and 50: Fluid Model Replacing random proces
Page 51: Starting Empty and Approaching Stat
Page 54 and 55: 8 350 300 250 200 150 100 50 Lambda
Page 56 and 57: What if Pr{Retrial} increases to 0.
Page 58: Bottleneck Analysis Inventory Build

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