The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
An ideal seed non-hierarchical clustering algorithm for cellular manufacturing
Abstract
SUMMARY This paper describes the development of a non-heuristic algorithm for solving group techology problems. The problem is first formulated as a bipartite graph, and then an expression for the upper limit to the number of groups is derived. Using this limit, a non-hierarchical clustering method is adopted for grouping components into families and machines into cells. After diagonally correlating the groups, an ideal-seed method is used to improve the initial grouping. A quantitative criterion called grouping efficiency is then developed for comparing alternative solutions. The algorithm and the criterion are demonstrated through an example.
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Citations
Cites
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Cited by
Data clustering: application and trends
Oyewole, G. J., & Thopil, G. A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10325-y · Cited by 312
Is production and operations management a discipline? A citation/co‐citation study
Pilkington, A., & Liston‐Heyes, C. (1999) · International Journal of Operations & Production Management · doi:10.1108/01443579910244188 · Cited by 188
Metadata
- Authors
- P CHANDRASEKHARAN, M., & RAJAGOPALAN, R.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 1986-03
- Volume
- 24
- Issue
- 2
- Pages
- 451-463
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W2147519951
- Cited by
- 441
- References
- 10
- FWCI
- 25.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- P CHANDRASEKHARAN, M., & RAJAGOPALAN, R. (1986). An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International Journal of Production Research. https://doi.org/10.1080/00207548608919741
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
