Abstract
Empirical studies considering the location and relocation of emergency medical services (EMS) vehicles in an urban region provide important insight into dynamic changes during the day. Within a time period of one day the demand, travel time/speed of ambulances and covered areas change cyclically. Nevertheless, most existing approaches in literature ignore these variations and require a (temporally and spatially) fixed (double) coverage of the planning area. Neglecting these variations and fixation of the coverage could lead to a false estimation of the time dependent fleet size and individual positioning of ambulances. Through extensive data collection, nowadays it is possible to precisely determine the required coverage of demand areas. Based on data driven optimization a new approach is presented, maximizing the flexible, empirically determined required coverage, which has been adjusted for variations due to day-time and site. This coverage prevents the EMS system from unavailability of ambulances due to parallel operations to ensure an improved coverage of the planning area closer to realistic demand. An integer linear programming model is formulated in order to locate and relocate ambulances. Based on a comprehensive case study it is shown, that the approach achieves these objectives and leads to cost savings and improvement in the quality of emergency care.
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