Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky

Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky

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Article ID: iaor1998240
Country: Netherlands
Volume: 75
Issue: 3
Start Page Number: 567
End Page Number: 581
Publication Date: Jun 1994
Journal: European Journal of Operational Research
Authors: ,
Keywords: artificial intelligence: decision support, location
Abstract:

Decreasing the elapsed time to respond to an emergency is a goal of Emergency Medical Services (EMS). The size of the ambulance fleet and the location of ambulances within the service area are two factors that the EMS planners can control; these two factors directly affect the system's response time and contribute to the attainment of this goal. In this paper, a maximal expected coverage location model with time variation (TIMEXCLP) is developed and integrated into a decision support system to aid EMS planners to allocate vehicles within their service area. In applying TIMEXCLP to the Louisville, Kentucky, EMS system, response time was decreased by 36%.

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