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A hybrid algorithm to size the hospital resources in the case of a massive influx of victims

机译:在大规模涌入受害者的情况下,杂交算法大小的尺寸

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Disaster situations either natural or made-man caused a large number of deaths and injured people. Morocco has experienced several disasters recently, the last one was the railway accident on 16 October 2018, which caused 127 serious injuries and 7 deaths. This large number was a big problem for the hospital to manage the received victims in right direction, which caused lives lost and disability. In this article, in collaboration with Mohammed (V) hospital in Casablanca city in Morocco, we suggested a solution that saves lives and eliminates number of disability by using a hybrid algorithm to size the hospital resources in the case of a massive influx of victims. We also suggested a support decision tool that is called Emergency Support Decision Tool. This helpful tool gives an idea about the needed resources that support these emergencies according to the victim’s number. The proposed solution consisted in making a hybrid algorithm that mixed the theoretical simulation process and the experience feedback by developing hybrid genetic and hybrid heuristic algorithms. These algorithms using as an input the matrix solutions that generated under ARENA software and the solution generated by neural networks that based on experiences feedback. The objective was to provide a solution based on available resources. In fact, the results showed that the hybrid heuristic algorithm is more performant than the hybrid genetic algorithm.
机译:灾难状况要么是自然的或制作人,导致了大量死亡和受伤的人。摩洛哥最近经历了几个灾难,最后一个是2018年10月16日的铁路事故,这导致了127名严重伤害和7人死亡。这个大量的人是医院管理所接受的受害者在正确的方向上导致生命失去和残疾的大问题。在本文中,与摩洛哥卡萨布兰卡市的穆罕默德(v)医院合作,我们建议通过在大规模涌入受害者涌入的情况下,通过使用混合算法来拯救生命并消除残疾的次数。我们还建议了一个称为紧急支持决策工具的支持决策工具。此有用的工具对根据受害者的号码提供支持这些紧急情况的所需资源。所提出的解决方案制备了一种混合理论模拟过程和通过开发混合遗传和混合启发式算法的理论模拟过程和经验反馈的混合算法。这些算法用作输入的矩阵解决方案,该解决方案在竞技场软件下生成的基于经验反馈的神经网络生成的解决方案。目标是提供基于可用资源的解决方案。事实上,结果表明,混合动力启发式算法比混合遗传算法更加性能。

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