An Application of Fuzzy ART Neural Network for the Part-Machine Grouping Problem ： modified algorithm and performance evaluation

• Main Authors: Chung-Lun Kuo, 郭仲倫
• Other Authors: Yu-Chuen Yeh;Chuen-Sheng Cheng
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/93084128239248906242

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.