Location and Allocation of Emergency Medical Services




Master of Science in Industrial Engineering (MSIE)


Mechanical Engineering

Document Type



EMS systems coordinate the equipment, facilities, and dispatch of medical teams to 911 calls within a geographic region. The time required for an ambulance to arrive at a caller’s location is an important performance metric for measuring the effectiveness of an EMS system. The distribution of ambulances throughout a region influence the response time. Discrete event simulation was used to model the urban East Baton Rouge Parish (EBR), Louisiana EMS system to assess alternatives and determine the ideal geographic distribution of ambulances for reducing response time. The region was clustered into equal area grids with a demand node centered in each grid. Nodes were weighted by historical proportion of calls received from each grid. Travel times were determined from probability distributions of historical travel times for each path. Alternative scenarios consisted of adding up to two ambulances to the system at existing stations. Average response time, 90% fractile response time, and standard deviation of response time were used to compare alternatives to the baseline and select the best alternative for the 1-median and 2-median solutions. Using historical travel times was effective in modeling the ambulance response system. The best 1-median solution was to add an ambulance to station 6 which projected to reduce the Code 3 90% fractile response time to 11.09 minutes, just over the 11 minute goal set by EBR EMS and the best 2-median solution was to add ambulances to station 6 and to stations 15 which were projected to bring the Code 3 90% fractile response time to 10.85 minutes, below the 11 minute goal. The model was able to predict which hospitals would have the greatest impact from an ED closure, but the model needs to be expanded to include the entire parish area and account for trends in inter-arrival times and call volumes over the long term in order to make accurate time predictions about large changes to the EMS system.



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Committee Chair

Harvey, Craig



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