多目标双重覆盖下的急救中心选址及其狼群算法求解

考虑到急救中心提供的急救服务的时效性以及服务的特殊性,通过优化急救中心选址和合理安排急救车数量,提高急救系统的运营效率,保证患者生命安全。结合实际情况对某地区当前急救服务进行改善,对当前急救中心进行优化,选址合适的候选点安排一定数量的救护车提供高效的急救服务,建立多目标双重覆盖模型,并设计狼群基因算法对其进行求解。算法采用游走、召唤、围攻等搜索方法,能够有效找到较优解,而且快速的邻域解的适应度计算方法确保了其搜索效率。以某区急救中心为研究对象,从候选急救机构中选择合适的医疗设施作为急救中心提供急救服务,并对每个急救中心的救护车数量进行管理,以满足居民日常急救就医需求,求解结果使区域医疗资源配置更加均衡,为探索建立分级诊疗和双向转诊机制打下基础。

The Location of Emergency Center under the Multi-objective Double-covering and Solving by Wolf Pack Algorithm

The ambulance location problem consists of determining which ambulance station should be open and how many ambulances should be allocated at this station. Since in reality circumstances time is crucial, one of the major issue in emergency conditions is to ensure a quick response of the rescue operations by an efficient vehicle location management. From a practical point of view, it is essential that emergency vehicles be located so as to ensure an adequate coverage of the interested area and the pre-hospital care system can provide a prompt rescue. Take account of issues above, covering modeling is applicable for solving ambulance location problem. however ,because of globally pervasive trends, such as decreasing birth rates, and increasing environmental growth , supplementary issues should be taken into consideration, instead of solely seek for maximizing covering demand. We formulate a improved programming model that determines the location of ambulance bases among a set of potential ones with three objectives involved: maximizing total demand covered twice within a predetermined distance parameter, minimizing the total distance between the uncovered allocated demands and the facilities to which they are assigned and minimize the cost of emergency facility location, including construction cost and labor cost. Then we illustrate the high efficiency of pre-hospital?system and first-aid station location brought, and the positive role which played in all kinds of emergency relief. Also to solve this NP-hard problem, we employ wolf pack algorithm to effectively get an ideally location result.