Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.