元胞制造系统的机器-零件分组:神经网络方法

Kyung Mi Lee, T. Yamakawa, Keon-Myung Lee
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引用次数: 4

摘要

机器单元形成问题是将机器分组到机器族中,将零件分组到零件族中,以最大限度地减少单元和柔性制造系统中的瓶颈机器、异常零件和单元间零件运动。本文提出了一种新的基于自适应汉明网络的机器细胞形成方法,这是一种神经网络模型。为了验证该方法的适用性,本文给出了一些实验结果,并与其他细胞形成方法进行了比较。实验结果表明,该方法能较好地解决机器细胞形成问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine-part grouping for cellular manufacturing systems: a neural network approach
The machine cell formation problem is about grouping machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts and inter-cell part movements in cellular and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net, which is a neural network model. To see the applicability of the method, this paper shows some experimental results and compares the proposed method with other cell formation methods. From the experiments, we can see that the proposed method can produce good cells for the machine cell formation problem.
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