Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson arrival processes
Input uncertainty quantification for simulation models with piecewise-constant non-stationary Poisson arrival processes
复制标题
具有分段常数非平稳泊松到达过程的仿真模型的输入不确定性量化
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
B. Nelson
中科院分区：
文献类型：
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作者：
Lucy E. Morgan;A. Titman;D. Worthington;B. Nelson
输入不确定性（IU）是使用由有限的现实世界数据估计的输入分布来驱动模拟模型的结果。当使用平稳输入分布时，已经有了量化IU的方法。在本文中，我们在此基础上进行了拓展，并提供了两种方法来量化由分段常数非平稳泊松到达过程驱动的模拟模型中的IU。文中给出了这些方法的数值评估和示例，结果表明这些方法效果良好。
Input uncertainty (IU) is the outcome of driving simulation models using input distributions estimated by finite amounts of real-world data. Methods have been presented for quantifying IU when stationary input distributions are used. In this paper we extend upon this work and provide two methods for quantifying IU in simulation models driven by piecewise-constant non-stationary Poisson arrival processes. Numerical evaluation and illustrations of the methods are provided and indicate that the methods perform well.