可视化系统用于识别OHT铁路网络中的结构脆弱环节,在半导体FAB中使用betweenness中心性
Jinwoo Choi1, Youngbin Park2, Yeeun Choi1
1Graduated School of Data Science, Seoul National University, Seoul, South Korea.
PloS one
|July 12, 2024
概括
本研究使用新型的中心性算法识别了半导体工厂运输网络中的关键环节. 视觉化系统有助于确定漏洞,以提高生产效率.
科学领域:
- 工业工程 工业工程 工业工程
- 计算机科学 计算机科学
- 运营研究 运营研究
背景情况:
- 半导体制造依赖于自动引导车辆 (AGV),例如用于晶圆运输的上空提升运输 (OHT).
- 现有的路由算法 (例如,Dijkstra的) 可以在复杂的OHT网络中造成拥堵和死锁.
- 识别结构上脆弱的环节对于优化FAB生产力至关重要.
研究的目的:
- 在半导体制造 (FAB) 内的铁路网络上空吊车运输 (OHT) 中识别结构脆弱的环节.
- 开发一个修改的betweenness中心性算法,考虑OHT网络的层次节点-端口结构.
- 创建一个基于Web的可视化系统,以加强有关网络漏洞的现场决策.
主要方法:
- 员工之间的中心性,以量化OHT网络的结构脆弱性.
- 修改了布兰德斯算法,以结合节点-端口特征而不会增加计算时间.
- 将并行化集成到修改后的算法中,以进一步减少计算时间.
- 开发了一个端到端的基于Web的可视化系统,用于计算和显示中心性结果.
主要成果:
- 开发了一种针对具有节点端口结构的OHT网络量身定制的新型中间中心性算法.
- 创建了一个功能性的基于Web的可视化系统,用于识别漏洞链接.
- 通过将结果与行业合作伙伴的历史识别的脆弱链接进行比较,验证了算法的有效性.
- 证明了节点-端口结构之间的中心性准确地识别了关键的网络点.
结论:
- 修改后的中间中心性算法有效地识别了半导体 FAB OHT 网络中的脆弱环节.
- 可视化系统显著提高了识别网络漏洞的实际应用性和相关性.
- 根据已识别的漏洞优化OHT网络结构可以提高FAB的生产率并减少截止日期.
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