FACT: A new neural network-based clustering algorithm for group technology
| Autor: | L. I. Burk, S. Kamal |
|---|---|
| Rok vydání: | 1996 |
| Předmět: |
Engineering
Fuzzy clustering business.industry Strategy and Management Correlation clustering Management Science and Operations Research Industrial and Manufacturing Engineering Group technology Data stream clustering CURE data clustering algorithm Canopy clustering algorithm FLAME clustering Artificial intelligence business Cluster analysis |
| Zdroj: | International Journal of Production Research. 34:919-946 |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/00207549608904943 |
| Popis: | This paper introduces the FACT (Fuzzy art with Add Clustering Technique) algorithm which is a new neural network-based clustering technique. FACT can be trained to cluster machines and parts for cellular manufacturing under a multiple objective environment. The existing GT clustering techniques are mainly concerned with grouping parts and machines based on only one criterion which is the parts' processing routes. The FACT algorithm is able to consider several similarity criteria such as parts' processing routes, design requirements of parts, processing time on each machine, and demand for each part. The FACT algorithm, which is based on the fuzzy ART neural network, is powerful enough to solve problems of real-world sized complexity. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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