期刊
IISE TRANSACTIONS
卷 51, 期 3, 页码 266-283出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/24725854.2018.1490046
关键词
Performance evaluation; simulation; analytical modeling; multi-fidelity regression; manufacturing systems
Simulation is considered as one of the most practical tools to estimate manufacturing system performance, but it is slow in its execution. Analytical models are generally available to provide fast, but biased, estimates of the system performance. These two approaches are commonly used distinctly in a sequential approach, or one as alternative to the other, for assessing manufacturing system performance. This article proposes a method to combine simulation experiments with analytical results in a single performance evaluation model. The method is based on kernel regression and allows considering more than one analytical methods. A high-fidelity model is combined with low-fidelity models for manufacturing system performance evaluation. Multiple area-based low-fidelity models can be considered for the prediction. The numerical results show that the proposed method is able to identify the reliability of low-fidelity models in different areas and provide estimates with higher accuracy. Comparison with alternative approaches shows that the method is more accurate in a studied manufacturing application.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据