Answers to EMS Queries About Dynamic Deployment: Fractile Performance, Cost, and Management
AuthorAljalahema, Rashid Shaheen
AdvisorGoldberg, Jeffrey B.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractDynamic deployment is an Emergency Medical Services (EMS) ambulance management strategy where 911 call demand coverage is maximized continuously through time. Unlike static deployment where dispatched ambulances leave a coverage gap until they return to their home-base after service, dynamic deployment redeploys idle ambulances to different locations if that leads to an increase in demand coverage. The purpose of this dissertation was to study dynamic deployment as a viable, beneficial, and cost-effective methodology in managing EMS ambulances and crews. The literature, while rich in studies on static deployment, was lacking when it came to ambulance management strategies like dynamic deployment. Through a discrete-event simulation model, hypothetical EMS systems were simulated under dynamic and static deployment with different demand patterns, demand loads, and system sizes. Dynamic deployment was found to be as good, or often better, in emergency response metrics than static deployment. When EMS systems want to meet a certain response goal, dynamic deployment may enable them to achieve that performance with fewer vehicles than static deployment. While savings in number of vehicles translate to substantial savings in crew wages and vehicular purchasing costs, dynamic deployment may increase operating costs per vehicle because of the extra mileage involved in redeployments. Many EMS systems with average vehicular utilizations of 40% to 50% may find, however, that dynamic deployment may be both cost-effective and beneficial in improving response performance. Different redeployment strategies were studied to address the added travel costs of dynamic deployment and a min-sum assignment model was found to decrease redeployment travel the most without impacting response performance. Finally, a procedure and a mathematical model were developed to route vehicles intelligently such that demand coverage is maximized throughout the redeployment process.
Degree ProgramGraduate College
Systems & Industrial Engineering