Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection
评估了四种单元形成方法（阵列法、非层次聚类、增广矩阵法和神经网络）在不同规模和缺陷程度数据集上的性能，发现ALC和神经网络优于ZODIAC，后者优于阵列法。
This study addresses the part‐machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array‐based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non‐hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p‐median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture‐model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry‐size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array‐based methods of ROC2 and DCA. Copyright © 1994, Wiley Blackwell. All rights reserved