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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/758
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| DC Field | Value | Language |
|---|
| contributor.author | Mahapatra, S S | - |
| contributor.author | Sahu, S | - |
| contributor.author | Sudhakarapandian, R | - |
| date.accessioned | 2008-12-24T02:56:37Z | - |
| date.available | 2008-12-24T02:56:37Z | - |
| date.issued | 2008 | - |
| identifier.citation | Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, 8-11 December 2008, Singapore P 1209-1213 | en |
| identifier.uri | http://hdl.handle.net/2080/758 | - |
| description | Copyright belongs to IEEE | en |
| description.abstract | The cell formation (CF) problem mainly deals
with clustering of parts into part families and the machines
into machine cells. The parts are grouped into part families
based on similarities in their manufacturing and design
attributes and the machines are allocated into machine cells
to produce the identified part families. The zero-one partmachine
incidence matrix is commonly used as input to any
clustering algorithm. The output is generated in the form of
block diagonal structure. Production data such as operation
time, sequence of operations, batch size etc. that have
significant bearing on smooth flow of materials are not
considered in such methods. In this paper, an attempt has
been made to develop an algorithm based on Adaptive
Resonance Theory (ART) neural network to addresses this
issue by considering combination of operation sequence and
operation time of the parts to enhance the quality of the
solution obtained for the CF problem. A new performance
measure is proposed to assess the goodness of the solution
quality obtained through proposed algorithm. The
performance of the proposed algorithm is tested with
example problems and the results are compared with the
existing methods found in the literature. The results
presented clearly shows that the performance of the
proposed algorithm is comparable with other methods for
small size problems and better for large size problems. | en |
| format.extent | 394963 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | IEEE | en |
| subject | Cell Formation | en |
| subject | ART1 | en |
| subject | Group Formation | en |
| title | ART based cell formation using combined operation sequence and time | en |
| type | Article | en |
| Appears in Collections: | Conference Papers
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| ssm-ieee-singapore.pdf | | 385Kb | Adobe PDF | View/Open |
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