A markov decision process model for the optimal dispatch of military medical evacuation assets
A markov decision process model for the optimal dispatch of military medical evacuation assets
复制标题
军事医疗后送资产优化调度的马尔可夫决策过程模型
DOI：
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发表时间：
2014
影响因子：
3.6
通讯作者：
B. Lunday
中科院分区：
文献类型：
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作者：
Sean K. Keneally;Matthew J. Robbins;B. Lunday
本文建立了一个马尔可夫决策过程（MDP）模型来研究作战环境下的空中医疗后送（MEDEVAC）调度策略。由于服务地点的威胁状况和每个伤亡事件的优先级，决定向每个服务请求派遣哪种航空医疗资产的问题变得复杂。我们假设医疗后送支持请求按顺序到达，并且在请求启动时就知道每个伤员的位置和优先级。美国军方使用9行医疗后送请求系统将伤亡人员分为三个优先级别：紧急，优先和常规。单个伤亡事件中可能存在多个伤亡人员，其中最高优先级的伤亡人员确定伤亡事件的优先级。此外，根据9行医疗后送请求所示的威胁程度，可能需要武装护送。所提出的MDP模型表明，如何最佳调度医疗后送直升机伤亡事件，以最大限度地提高稳态系统的效用。从服务特定请求中获得的效用取决于伤亡人数、每个伤亡人员的优先级以及服务的救护直升机和伤亡事件的位置。采用相对值迭代动态规划算法求解调度问题的最优解。计算的例子被用来调查不同的威胁情况下的最佳调度政策和武装护送延误的例子是基于作战方案，美国陆军医疗后送部队支持在阿富汗的地面行动。
We develop a Markov decision process (MDP) model to examine aerial military medical evacuation (MEDEVAC) dispatch policies in a combat environment. The problem of deciding which aeromedical asset to dispatch to each service request is complicated by the threat conditions at the service locations and the priority class of each casualty event. We assume requests for MEDEVAC support arrive sequentially, with the location and the priority of each casualty known upon initiation of the request. The United States military uses a 9-line MEDEVAC request system to classify casualties as being one of three priority levels: urgent, priority, and routine. Multiple casualties can be present at a single casualty event, with the highest priority casualty determining the priority level for the casualty event. Moreover, an armed escort may be required depending on the threat level indicated by the 9-line MEDEVAC request. The proposed MDP model indicates how to optimally dispatch MEDEVAC helicopters to casualty events in order to maximize steady-state system utility. The utility gained from servicing a specific request depends on the number of casualties, the priority class for each of the casualties, and the locations of both the servicing ambulatory helicopter and casualty event. Instances of the dispatching problem are solved using a relative value iteration dynamic programming algorithm. Computational examples are used to investigate optimal dispatch policies under different threat situations and armed escort delays; the examples are based on combat scenarios in which United States Army MEDEVAC units support ground operations in Afghanistan.