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首页> 外文期刊>Jordan Journal of Mechanical and Industrial Engineering >Formation of Machine Cells/Part Families in Cellular Manufacturing Systems Using an ART-Modified Single Linkage Clustering Approach – A Comparative Study
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Formation of Machine Cells/Part Families in Cellular Manufacturing Systems Using an ART-Modified Single Linkage Clustering Approach – A Comparative Study

机译:使用ART修正的单连接聚类方法在蜂窝制造系统中形成机器单元/零件族的比较研究

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This paper proposes an Art Modified Single Linkage Clustering approach (ART-MOD-SLC) to solve cell formation problems in Cellular Manufacturing. In this study, an ART1 network is integrated with Modified Single Linkage Clustering (MOD-SLC) to solve cell formation problems. The Percentage of Exceptional Elements (PE), Machine Utilization (MU), Grouping Efficiency (GE) and Grouping Efficacy (GC) are considered as performance measures. This proposed heuristic ART1 Modified Single Linkage Clustering (ART-MOD-SLC) first constructs a cell formation using an ART1 and then refines the solution using Modified Single Linkage Clustering (MOD-SLC) heuristic. ART1 Modified Single Linkage Clustering has been applied to most popular examples in the literature including a real time manufacturing data. The computational results showed that the proposed heuristic generates the best solutions in most of the examples. The proposed method is compared with the well-known clustering approaches selected from the literature namely ROC2, DCA, SLC and MOD-SLC. Comparison and evaluations are performed using four performance measures. Finally analysis of results is carried out to test and validate the proposed ART-MOD-SLC approach. The MCF methods considered in this comparative and evaluative study belong to the cluster formation approaches and have been coded by using C++ with an Intel P-IV compatible system.
机译:本文提出了一种Art Modified Single Linkage聚类方法(ART-MOD-SLC),以解决细胞制造中的细胞形成问题。在这项研究中,ART1网络与修改的单链接聚类(MOD-SLC)集成在一起以解决细胞形成问题。异常要素(PE),机器利用率(MU),分组效率(GE)和分组效率(GC)的百分比被视为性能指标。这项拟议的启发式ART1修饰单链接聚类(ART-MOD-SLC)首先使用ART1构建细胞形成,然后使用修正单链接聚类(MOD-SLC)启发式改进解决方案。 ART1修改的单链接聚类已应用于包括实时制造数据在内的文献中最流行的示例。计算结果表明,所提出的启发式方法在大多数示例中均产生了最佳解决方案。将该方法与从文献中选择的众所周知的聚类方法(ROC2,DCA,SLC和MOD-SLC)进行了比较。比较和评估使用四个绩效指标进行。最后,对结果进行分析,以测试和验证所提出的ART-MOD-SLC方法。在此比较和评估研究中考虑的MCF方法属于集群形成方法,并且已通过将C ++与Intel P-IV兼容系统一起使用进行了编码。

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