@inproceedings{76ef4488be8847388558ec7b02c5b494,
title = "Part-machine clustering: The comparison between adaptive resonance theory neural network and ant colony system",
abstract = "The aim of part-machine clustering (PMC) in cellular manufacturing systems is to cluster parts that have similar processing requirements into part-families; and machines that meet these requirements into machine-groups. Although PMC problems are known as NP-complete in the literature, extensive research is still conducted in this field because of the considerable practical value of PMC for industries. In this paper, conventional adaptive resonance theory (ART1) neural network method and a novel meta-heuristic approach called ant colony system (ACS) are proposed for solving PMC problems. The experimental results show that ACS performs better than ART1 neural network on the same selected benchmark test problems. A PMC performance measure called grouping efficiency (GE) is also employed to evaluate the clustering result.",
keywords = "Adaptive resonance theory neural network, Ant colony system, Group technology, Grouping efficiency, Part-machine clustering",
author = "Bo Xing and Gao, {Wen Jing} and Nelwamondo, {Fulufhelo V.} and Kimberly Battle and Tshilidzi Marwala",
year = "2010",
doi = "10.1007/978-3-642-12990-2_87",
language = "English",
isbn = "9783642129896",
series = "Lecture Notes in Electrical Engineering",
pages = "747--755",
booktitle = "Advances in Neural Network Research and Applications",
note = "7th International Symposium on Neural Networks, ISNN 2010 ; Conference date: 06-06-2010 Through 09-06-2010",
}