| Summary: | 碩士 === 元智大學 === 工業工程研究所 === 83 === Group technology (GT) is one of the key issues in a
successful implementation of flexible manufacturing systems
(FMSs) . A major benefit of GT is the simplification of the
material flow within the shop.This fact coupled with reduced
set-up times , which result from part similarities , yields
shorter lead times and lower work-in-process . This study
investigates the application of Fuzzy ART neural network to
the part-machine grouping problem in GT . Fuzzy ART neural
network provides a framework for both binary and continuous
values , and offers several advantages , particularly the
reduction in computatioal complexity and ability to handle
large scale industrial problems . One weakness of this
approach is that the quality of a grouping solution is
mainly dependent on the initial disposition of part-machine
incidence matrix especially in the presence of bottleneck
machines . A modified Fuzzy ART neural network has been
developed to enhance the Fuzzy ART neural network in
part-machine grouping problem . In this study,there are two
measures used to evaluate the quality of solutions given by a
cell formation algorithm.They are grouping efficiency and the
number of exceptional elements.
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