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http://hdl.handle.net/2080/736
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| DC Field | Value | Language |
|---|
| contributor.author | Sudhakarapandian, R | - |
| contributor.author | Mahapatra, S S | - |
| date.accessioned | 2008-10-22T05:40:19Z | - |
| date.available | 2008-10-22T05:40:19Z | - |
| date.issued | 2008 | - |
| identifier.citation | Computers & Industrial Engineering (Accepted Version) | en |
| identifier.uri | http://dx.doi.org/10.1016/j.cie.2008.08.003 | - |
| identifier.uri | http://hdl.handle.net/2080/736 | - |
| description | Copyright for the published version belongs to Elsevier | en |
| description.abstract | Batch type production strategies need adoption of cellular manufacturing (CM) in
order to improve operational effectiveness by reducing manufacturing lead time
and costs related to inventory and material handling. CM necessitates that parts
are to be grouped into part families based on their similarities in manufacturing
and design attributes. Then, machines are allocated into machine cells to
produce the identified part families so that productivity and flexibility of the
system can be improved. Zero-one part-machine incidence matrix (PMIM)
generated from route sheet information is commonly presented as input for
clustering of parts and machines. An entry of ‘1’ in PMIM indicates that the part is
visiting the machine and zero otherwise. The output is generated in the form of
block diagonal structure where each block represents a machine cell having
more than one machines and a part family. The major limitations of this approach
lies in the fact that important production factors like operation time, sequence of
operations, and lot size of the parts are not accounted for. In this paper, an
attempt has been made to propose a clustering methodology based on adaptive
resonance theory (ART) for addressing these issues. Initially, a methodology
considering only the operation sequence of the parts has been proposed. Then,
the methodology is suitably modified to deal with combination of operation
sequence and operation time of the parts to address generalized cell formation
(CF) problem. A new performance measure is proposed to quantify the
performance of the proposed methodology. The performance of the proposed
algorithm is tested with benchmark problems from open literature and the results
are compared with the existing methods. The results clearly indicate that the
proposed methodology outperforms the existing methods in most cases. | en |
| format.extent | 377948 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | Elsevier | en |
| subject | Cell Formation | en |
| subject | Group Efficiency | en |
| subject | Exceptional elements | en |
| title | Manufacturing cell formation with production data using neural networks | en |
| type | Article | en |
| Appears in Collections: | Journal Articles
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Files in This Item:
| File |
Description |
Size | Format |
|---|
| ssm-2008-sudhakar.pdf | | 369Kb | Adobe PDF | View/Open |
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