Machine-part family formation with the adaptive resonance theory paradigm
作者:
C. DAGLI,
R. HUGGAHALLI,
期刊:
International Journal of Production Research
(Taylor Available online 1995)
卷期:
Volume 33,
issue 4
页码: 893-913
ISSN:0020-7543
年代: 1995
DOI:10.1080/00207549508930185
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The ARTI neural network paradigm employs a heuristic where new vectors arc compared with group representative vectors for classification. ARTI is adapted for the cell formation problem by reordering input vectors and by using a better representative vector. This is validated with both test cases studied in literaure as well as synthetic matrices. Algoriihmns for effective use of ARTI are proposed. This approach is observed to produce sufficiently accurate results and is therefore promising in both speed and functionality. For the automatic generation of an optimal family formation solution a decision support system can be integrated with ARTI.
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