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Comparison Of Analysis Strategies For Screening Designs In Large-scale Computer Simulation Models

机译：大型计算机仿真模型中筛选设计的分析策略比较

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In large-scale computer simulation models it is often necessary to perform a screening experiment to reduce the number of factors to be examined in subsequent analysis. This study evaluated the results of a Plackett- Burman screening design using three different analysis strategies: 1) an approach due to Box and Meyer (1993); 2) an approach due to Harnada and Wu (1992); and 3) a standard Response Surface Methodology (RSM) approach. These strategies or methodologies were used to identify the active/significant factors across 17 different model outputs. The results from these three methodologies were then compared against each other for any notable differences in the identified significant factors. In one instance, where there was a notable difference, further analysis was performed in an attempt to ascertain which methodology was the best predictor for that specific response. A Resolution V design was used in this subsequent analysis to produce a validation model, which was then used to compare the three initial analysis strategies. The strategy/methodology that produced the model with the smallest mean absolute percent error (MAPE), the measurement criteria, was selected as the best for that response.
机译：在大型计算机仿真模型中，通常需要执行筛选实验以减少在后续分析中要检查的因素的数量。这项研究使用三种不同的分析策略评估了Plackett-Burman筛查设计的结果：1）Box and Meyer（1993）提出的方法; 2）Harnada和Wu（1992）提出的方法;和3）标准响应面方法（RSM）方法。这些策略或方法用于识别17种不同模型输出中的活跃/重要因素。然后，将这三种方法的结果进行比较，以比较发现的重要因素之间的任何显着差异。在一个实例中，存在显着差异的地方，进行了进一步的分析，以试图确定哪种方法是该特定反应的最佳预测指标。在后续的分析中使用了Resolution V设计，以生成验证模型，然后将其用于比较三种初始分析策略。选择产生具有最小平均绝对百分比误差（MAPE）的模型（测量标准）的策略/方法作为对该响应的最佳选择。

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