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A high-speed algorithm for repairable stochastic flow networks with converging flows from multiple sources

机译:具有来自多个源的会聚流的可修复随机流网络的高速算法

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An algorithm and software implementation have been developed for a very fast discrete-event simulator for determining the losses from failures in repairable stochastic flow networks with converging flows from multiple sources. This paper shows that the computational speed related to determining the variation of the flow through a stochastic flow network can be improved enormously if the topology of the network is exploited directly. The proposed method is based on new results related to maximising the flow in networks with converging flows. It handles repairable networks with multiple sources of production flow, multi-commodity flows, overlapping failures, multiple failure modes, redundant components and redundant branches of components. The simulator is capable of tracking the cumulative distribution of the potential losses from failures associated with the whole network and with each component in the network. Finally, by using an exemplary stochastic flow network with converging flows, it is demonstrated rnhow the developed fast discrete-event simulator can be used for revealing the distribution of the operational losses and identifying the components with the largest contributions to the total losses from failures.
机译:已经开发了用于非常快速的离散事件模拟器的算法和软件实现,该模拟器用于确定来自多个源的会聚流可修复的随机流网络中的故障造成的损失。本文表明,如果直接利用网络的拓扑结构,可以极大地提高与确定通过随机流动网络的流量变化有关的计算速度。所提出的方法基于与在具有收敛流的网络中最大化流有关的新结果。它处理具有多种生产流,多商品流,重叠故障,多种故障模式,冗余组件和组件冗余分支的可修复网络。该模拟器能够跟踪与整个网络以及网络中每个组件相关的故障所引起的潜在损失的累积分布。最后,通过使用具有收敛流的示例性随机流网络,证明了开发的快速离散事件模拟器可用于揭示运行损失的分布并确定对故障造成的总损失贡献最大的组件。

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