用半马尔可夫决策优化海空医疗救援调度，提升响应效率。
Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms
- 将海空转运点建模为半马尔可夫决策过程，结合飞行器与舰船状态动态决策。
- 仿真显示，最优策略比无转运点方案快35%，比贪心策略快40%。
- 首次实测验证：美军陆军后勤支援舰配合两架直升机完成真人模型转运。
在海上环境中，通过航行中的水面舰船实现两架飞机间的患者转运，可显著提升医疗救援的覆盖范围与灵活性。然而，从多个航行中的舰船中选择合适的转运点，需综合考虑参与飞机的历史使用情况以及舰船的位置和速度。本文将该问题建模为包含固定陆地与移动舰船转运点的动作空间的半马尔可夫决策过程，并采用带根部并行化的蒙特卡洛树搜索来选择最优转运点并确定飞机调度时间。通过模拟调整模型参数，识别出代表性场景下舰船转运点能有效缩短应急响应时间。结果表明，采用舰船转运点的最优策略比无转运点的最优策略快35%，比贪心策略快40%。在与美国陆军合作下，首次实地部署了舰船转运点：在夏威夷欧胡岛以南，成功执行了一次模拟患者转运，由两架HH-60M医疗救护直升机与一艘美军后勤支援舰协同完成。所有直升机均按优化策略调度起飞。
原文摘要 · Abstract (English)
The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The selection of any one of multiple underway watercraft for patient exchange is complicated by participating aircraft utilization history and a participating watercraft position and velocity. The selection problem is modeled as a semi-Markov decision process with an action space including both fixed land and moving watercraft exchange points. Monte Carlo tree search with root parallelization is used to select optimal exchange points and determine aircraft dispatch times. Model parameters are varied in simulation to identify representative scenarios where watercraft exchange points reduce incident response times. We find that an optimal policy with watercraft exchange points outperforms an optimal policy without watercraft exchange points and a greedy policy by 35% and 40%, respectively. In partnership with the United States Army, we deploy for the first time the watercraft exchange point by executing a mock patient transfer with a manikin between two HH-60M medical evacuation helicopters and an underway Army Logistic Support Vessel south of the Hawaiian island of Oahu. Both helicopters were dispatched in accordance with our optimized decision strategy.
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