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don't solve manually , use Excel QM window and show screenshots to solve below question . St. Michael's Hospital and Bridgepoint Hospital are in the
don't solve manually , use Excel QM window and show screenshots to solve below question .
St. Michael's Hospital and Bridgepoint Hospital are in the process of developing an emergency response
plan in the event of a major disaster at George Brown College. Disasters can be triggered by a number
of event scenarios (i.e. weather, building collapse, fire, dangerous intruder, etc.) The main purpose of
this emergency preparedness plan is focused on the transportation of disaster victims from the GBC
campus to the two main hospitals in the area (St. Michael's and Bridgepoint). When a disaster occurs on
the GBC campus, vehicles can be dispatched from local fire and ambulance locations, as well as hospitals
and police departments where victims are brought to a staging area near the scene and await transport
to one of the two area hospitals. Aspects of the project analysis include the waiting times victims might
experience at the disaster scene for emergency vehicles to transport them to the hospital, and waiting
times for treatment once victims arrive at the hospital. The project team is analyzing various waiting
line models as follows. (Unless stated otherwise, arrivals are Poisson distributed, and service times are
exponentially distributed)
A. First, consider a single-server waiting line model in which the available emergency vehicles are
considered to be the server. Assume that victims arrive at the staging area ready to be
transported to a hospital on average every 7 minutes and that emergency vehicles are plentiful
and available to pick up and transport victims every 4.5 minutes. Compute the average waiting
time for victims. Next assume that the distribution of service times is undefined, with a mean of
4.5 minutes and a standard deviation of 5 minutes. Compute the average waiting time for the
victims.
B. Next consider a multiple-server model in which there are eight emergency vehicles available for
transporting victims to the hospitals, and the mean time required for a vehicle to pick up and
transport a victim to a hospital is 20 minutes. (Assume the same arrival rate as in Part !.)
Compute the average waiting line, the average waiting time for a victim, and the average time in
the system for a victim (waiting and being transported)
C. For the multiple-server model in Part B., now assume that there are a finite number of victims,
18. Determine the average waiting line, the average waiting time, and the average time in the
system.
D. From the two hospitals' perspectives, consider a multiple-server model in which the two
hospitals are servers. The emergency vehicles at the disaster scene constitute a single waiting
line, and each driver calls ahead to see which hospital is most likely to admit the victim first, and
travels to that hospital. Vehicles arrive at a hospital every 8.5 minutes, no average, and the
average service time for the emergency staff to admit and treat a victim is 12 minutes.
Determine the average waiting line for victims, the average waiting time, and the average time
in the system.
E. Next, consider a single hospital, S. Michael's, which in an emergency disaster situation has 5
physicians with supporting staff available. Victims arrive at the hospital on average every 8.5
minutes. It takes an emergency room team, on average, 21 minutes to treat a victim.
Determine the average waiting line, the average waiting time, and the average time in the
system.
F. For the multiple-server model in Part E., now assume that there are a finite number of victims,
23. Determine the average waiting line, the average waiting time, and the average time in the
system.
G. Which of these waiting line models do you think would be the most useful in analyzing a disaster
situation? How do you think some, or all , of the models might be used together to analyze a
disaster situation? What other types of waiting line models do you think might be useful in
analyzing a disaster situation?
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