论文标题
用于优化机队安排空气救护车的算法
Algorithms for Optimizing Fleet Scheduling of Air Ambulances
论文作者
论文摘要
适当的空气资产安排可能是患者生与死之间的区别。尽管在医院转移期间，安排较差可能会出现令人难以置信的问题，但在灾难的情况下，这可能是灾难性的。在人口分散的空气紧急医疗服务（EMS）系统的情况下，这些问题会放大，并且资源受到限制。尽管有足够大的问题空间，但实际的计算时间可能非常重要，但仍有确切的方法进行调度任务。对于这项研究，已知的空气和卫生设施坐标与配制的整数线性编程模型一起使用。这是通过Gurobi编程的，因此可以将性能与自定义算法解决方案进行比较。开发了两种方法，一种基于邻里搜索，另一种是在禁忌搜索上。尽管两者都能够达到与Gurobi解决方案相当接近的结果，但Tabu搜索的表现优于以前的算法。此外，它能够在大大减少的时间内这样做，而古罗比实际上无法在更大的示例中决心最佳。还通过计算统一设备体系结构（CUDA）开发并行的变化，尽管鉴于样本量较小，但并不能改善时间。
Proper scheduling of air assets can be the difference between life and death for a patient. While poor scheduling can be incredibly problematic during hospital transfers, it can be potentially catastrophic in the case of a disaster. These issues are amplified in the case of an air emergency medical service (EMS) system where populations are dispersed, and resources are limited. There are exact methodologies existing for scheduling missions, although actual calculation times can be quite significant given a large enough problem space. For this research, known coordinates of air and health facilities were used in conjunction with a formulated integer linear programming model. This was the programmed through Gurobi so that performance could be compared against custom algorithmic solutions. Two methods were developed, one based on neighbourhood search and the other on Tabu search. While both were able to achieve results quite close to the Gurobi solution, the Tabu search outperformed the former algorithm. Additionally, it was able to do so in a greatly decreased time, with Gurobi actually being unable to resolve to optimal in larger examples. Parallel variations were also developed with the compute unified device architecture (CUDA), though did not improve the timing given the smaller sample size.
