Metric Learning for Simulation Analytics
Metric Learning for Simulation Analytics
复制标题
DOI：
10.1109/wsc48552.2020.9383904
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
G. Laidler;Lucy E. Morgan;B. Nelson;N. Pavlidis
中科院分区：
文献类型：
--
作者：
G. Laidler;Lucy E. Morgan;B. Nelson;N. Pavlidis
随机模拟生成的样本路径在每次重复中往往表现出显著的变异性，会同时呈现出性能良好和不佳的阶段。因此，对总体性能指标的传统汇总忽略了对操作系统行为更细致的洞察。在本文中，我们从模拟分析的角度看待输出分析，借助机器学习方法从动态样本路径中揭示关键见解。我们提出了一个基于系统状态信息的k近邻模型，以促进对随机性能指标的实时预测。该模型建立在对状态观测之间特定于系统的相似性度量的前提下，我们通过度量学习来确定这种相似性。我们在一个随机活动网络和一个晶圆制造设施上对我们的方法进行了评估，这两者都使我们对度量学习提供解释和提高预测性能的能力有信心。
The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similarity between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.