Degel Dirk, Wiesche Lara, Rachuba Sebastian, Werners Brigitte
Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.
Health Care Manag Sci. 2015 Dec;18(4):444-58. doi: 10.1007/s10729-014-9271-5. Epub 2014 Mar 8.
Empirical studies considering the location and relocation of emergency medical service (EMS) vehicles in an urban region provide important insight into dynamic changes during the day. Within a 24-hour cycle, the demand, travel time, speed of ambulances and areas of coverage change. 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 an inaccurate estimation of the time-dependent fleet size and individual positioning of ambulances. Through extensive data collection, now 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. The use of such a programming model is supported by a comprehensive case study, which strongly suggests that through such a model, these objectives can be achieved and lead to greater cost-effectiveness and quality of emergency care.
针对城市地区紧急医疗服务（EMS）车辆的位置及重新定位开展的实证研究，为日间的动态变化提供了重要见解。在24小时周期内，救护车的需求、行驶时间、速度及覆盖区域都会发生变化。然而，文献中大多数现有方法忽略了这些变化，且要求对规划区域进行（时间和空间上）固定的（双重）覆盖。忽略这些变化以及覆盖范围的固定可能导致对随时间变化的车队规模及救护车个体定位的估计不准确。通过广泛的数据收集，现在能够精确确定需求区域所需的覆盖范围。基于数据驱动的优化，提出了一种新方法，即最大化灵活的、根据经验确定的所需覆盖范围，该范围已针对白天和地点的变化进行了调整。这种覆盖范围可防止EMS系统因并行作业导致救护车无法使用，以确保更接近实际需求地改善规划区域的覆盖情况。为了对救护车进行定位和重新定位，制定了一个整数线性规划模型。一项全面的案例研究支持了这种规划模型的使用，该研究有力地表明，通过这样一个模型，可以实现这些目标，并带来更高的成本效益和紧急护理质量。
