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首页> 外文期刊>IEEE Transactions on Automatic Control >Multifidelity Modeling for Analysis and Optimization of Serial Production Lines
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Multifidelity Modeling for Analysis and Optimization of Serial Production Lines

机译：串行生产线分析与优化的多倍性建模

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摘要

Recent advances in sensing, data analytics, and manufacturing technologies (e.g., 3-D printing, soft robotics, nanotechnologies, etc.) provide the potential to produce highly customized products by allowing flexible system design, endless device configurations, and unprecedented information flows. These opportunities also increase the complexity of controlling such systems optimally, which typically requires fast exploration of an increasingly large number of alternative operation strategies. Simulation and stochastic models have been particularly successful to support control and optimization of production systems, and methods have been developed to exploit them separately. Herein, we argue that the simultaneous use of these models can allow for better control and optimization by balancing the simulation accuracy, and related high computational costs, with the computational efficiency and lower accuracy of stochastic models. In this article, we assume that high fidelity models have higher accuracy and computational costs, and we present a novel multifidelity approach, which utilizes several models at different levels of fidelity to efficiently and effectively estimate and optimize the performance of asynchronous serial production lines with machines suffering from multiple failure types. Experimental results show that the multifidelity approach leads to better estimations, requiring less computational effort for optimization compared with the use of only high fidelity simulations.
机译：感测，数据分析和制造技术（例如，3-D打印，软机器人，纳米技术等）的最新进展提供了通过允许灵活的系统设计，无穷无尽的设备配置和前所未有的信息流来生产高度定制产品的潜力。这些机会也提高了最佳控制这些系统的复杂性，这通常需要快速探索越来越大的替代操作策略。模拟和随机模型特别成功地支持生产系统的控制和优化，并开发了方法以分别利用它们。在此，我们认为这些模型的同时使用可以通过平衡模拟精度和相关的高计算成本，以计算效率和随机模型的准确度来更好地控制和优化。在本文中，我们假设高保真模型具有更高的准确性和计算成本，并且我们提出了一种新的多思度方法，它利用了不同程度的保真度的多种型号，以有效且有效地估计和优化了与机器异步串行生产线的性能患有多种故障类型。实验结果表明，与仅使用高保真仿真相比，多尺寸方法导致更好的估计，需要较少的计算工作来优化。

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