Analyzing Disaster Situations at Tech Two area hospitals have jointly initiated several planning projects to determine how effectively their emergency facilities can handle disaster-related situations at nearby Tech University. These disasters could be weather related (such as a tornado), a fire, accidents (such as a gas main explosion or a building collapse), or acts of terrorism. One of these projects has focused on the transport of disaster victims from the Tech campus to the two hospitals in the area, Montgomery Regional and Radford Memorial. When a disaster occurs at Tech, emergency vehicles are dispatched from Tech police, local EMT units, hospitals, and local county and city police departments. Victims are brought to a staging area near the disaster scene and wait for 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.) 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. (Note that a finite calling population model with multiple servers will require the use of the QM for Windows software.) 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 type(s) of waiting line model(s) do you think might be useful in analyzing a disaster situation?
Analyzing Disaster Situations at Tech
Two area hospitals have jointly initiated several planning projects to determine how effectively their emergency facilities can handle disaster-related situations at nearby Tech University. These disasters could be weather related (such as a tornado), a fire, accidents (such as a gas main explosion or a building collapse), or acts of terrorism. One of these projects has focused on the transport of disaster victims from the Tech campus to the two hospitals in the area, Montgomery Regional and Radford Memorial. When a disaster occurs at Tech, emergency vehicles are dispatched from Tech police, local EMT units, hospitals, and local county and city police departments. Victims are brought to a staging area near the disaster scene and wait for 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.)
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. (Note that a finite calling population model with multiple servers will require the use of the QM for Windows software.)
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 type(s) of waiting line model(s) do you think might be useful in analyzing a disaster situation?
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