Optimization and implementation of cellular manufacturing system in a pump industry using three cell formation algorithms

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Abstract

In recent years cellular manufacturing has become an effective tool for improving productivity. Attainment of full benefits of cellular manufacturing depends firstly on the design of the machine cells and part families and secondly on the method of operation which take full advantages of cell properties. Inappropriate methods of loading and scheduling can even lead to the failure of cellular manufacturing systems (CMS), however efficiently the cell is designed. This paper examines three array-based clustering algorithms, namely rank order clustering (ROC), rank order clustering-2 (ROC2) and direct clustering analysis (DCA) for manufacturing cell formation, with a real-life example to demonstrate the effectiveness of various clustering algorithms. The machine cell formation methods considered in this comparative and evaluative study belongs to the cluster formation approach of solving the MCF problem. The most effective method is selected and used to build the cellular manufacturing system. The comparison and evaluation are performed using four published performance measures and compares the improvements with the existing conventional system and the cellular manufacturing system. The above algorithms were written in the C++ language on an Intel/Pentium III-PC-compatible system.