Machine-Part Family Formation with the Adaptive Resonance Theory Paradigm

Author

Cihan H. Dagli, Missouri University of Science and TechnologyFollow
R. Huggahalli

Abstract

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. © 1995 Taylor & Francis Group, LLC.

Recommended Citation

C. H. Dagli and R. Huggahalli, "Machine-Part Family Formation with the Adaptive Resonance Theory Paradigm," International Journal of Production Research, vol. 33, no. 4, pp. 893 - 913, Taylor and Francis Group; Taylor and Francis, Jan 1995.

The definitive version is available at https://doi.org/10.1080/00207549508930185

Department(s)

Engineering Management and Systems Engineering

International Standard Serial Number (ISSN)

1366-588X; 0020-7543

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

01 Jan 1995