空中救护车队优化调度算法
Algorithms for Optimizing Fleet Scheduling of Air Ambulances
摘要：
航空资产的适当调度可能是病人生与死的区别。虽然在医院转移期间，糟糕的调度可能会带来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。这些问题在空中紧急医疗服务（EMS）系统的情况下被放大，其中人口分散，资源有限。存在用于调度任务的精确方法，尽管给定足够大的问题空间，实际计算时间可能相当重要。在这项研究中，已知的空气和卫生设施的坐标与制定整数线性规划模型结合使用。这是通过Guidelines编程的，因此可以将性能与自定义算法解决方案进行比较。两种方法，一个基于邻域搜索和禁忌搜索。虽然两者都能够实现非常接近Guesthouse解决方案的结果，但Tabu搜索优于前一种算法。此外，它能够在大大减少的时间内完成这一任务，而Guesthouse实际上无法在较大的示例中解决最佳问题。并行变化也与计算统一设备架构（CUDA）一起开发，但由于样本量较小，没有改善时序。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
