Abu Dhabi UniversityMEM 506MEM506 - reportMEM506: MODELING & SIMULATION &OPERATION RESEARCHCARE FACILITY TREATS NONEMERGENCY PATIENTS CASE Description 4-27An acute -care facility treats non-emergency patients (cuts, colds, etc.) Patientsarrive according to an exponential interarrival-time distribution with a mean of 11 (alltime are in min). Upon arrival they check in at a registration desk staf
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MEM506 - report
MEM506: MODELING & SIMULATION &
OPERATION RESEARCH
CARE FACILITY TREATS NONEMERGENCY PATIENTS
CASE Description 4-27
An acute -care facility treats non-emergency patients (cuts, colds, etc.) Patients
arrive according to an exponential interarrival-time distribution with a mean of 11 (all
time are in min). Upon arrival they check in at a registration desk staffed by a single
nurse. Registration time follow a triangular distribution with parameters 6, 10, and 19.
After completing registration, they wait for an available examination room; there are
three identical such rooms. Data show that 55% of the patients have service times that
follow a triangular service time distribution with parameters 14, 22 and 39. The rest of
the patients (45%) have a triangular service time distribution with parameters 24, 36 and
59. Upon completion, patients are sent home.
The facility is open 16 h each day.
Solution
By using ARENA software model, we start with "Create" module using EXPO (11)
interarrival-times. For complicated models, first we can't control our measured variables
so we start an "Assign" module to initialize our arrival time. This sets up tracking with a
"Record" block at the. Then, we have check-in process module. This followed by a
"Decide" module which makes the assignment probabilistically. The "Assign" modules
the follow specify the specific service distributions for each type. Then, we terminate
with "Process" module and "Record" and "Dispose" module sequence. The architecture
is in Fig. 1.1
As usual, we base our estimated expected values on 20 replications or more with each
replication representing a single simulated day .The result estimated average total
patients time in system is 103.4 min and that by quote "+or-" half widths on our
estimates.
Fig. 1.1
Consider that the model in Fig. 1.1 might be used to explore the effects of factors
such as the number of examination rooms and the number of open hour for the clinic on
response including the average total patient time in the system.
CASE Description 4-28
For the second part of problem 7, lunch breaks for the doctors who staff the
examination rooms should be included. There are three doctors on duty for the first 3.5
hours of each 8 hours shift. For the next 90 minutes, the doctors take rotating 30
minutes lunch breaks, resulting in only two doctors being available at any point during
these 90 minutes. If all three doctors area busy with patients when the 90-minute lunch
period begins, they wait until the first patient among the three is don’t, and that doctor
takes the first 30-minute lunch break. After all the lunch breaks end, all three doctors are
available until the end of their eight-hour shift, at which point the second eight-hour
shift begins with the same staffing and lunch.
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