Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting the best flexible manufacturing systems (FMS) from a group of candidate FMSs. Multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been de...
| Main Authors: | , , , |
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| Format: | Citation Index Journal |
| Language: | English |
| Published: |
2007
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/127/ http://scholars.utp.edu.my/id/eprint/127/1/paper.pdf |
| _version_ | 1848658917412831232 |
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| author | P., Vasant A., Bhattacharya A., Abraham C., Grosan |
| author_facet | P., Vasant A., Bhattacharya A., Abraham C., Grosan |
| author_sort | P., Vasant |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting the best flexible manufacturing systems (FMS) from a group of candidate FMSs. Multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the "best candidate FMS alternative" from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, viz., design parameters, economic considerations, etc., affecting the FMS selection process under multiple, conflicting-in-nature criteria environment. The selection of FMS is made according to the error output of the results found from the proposed MCDM model.
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| first_indexed | 2025-11-13T07:22:09Z |
| format | Citation Index Journal |
| id | oai:scholars.utp.edu.my:127 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:22:09Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:1272017-01-19T08:27:12Z http://scholars.utp.edu.my/id/eprint/127/ Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker P., Vasant A., Bhattacharya A., Abraham C., Grosan Q Science (General) QA75 Electronic computers. Computer science This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting the best flexible manufacturing systems (FMS) from a group of candidate FMSs. Multi-criteria decision-making (MCDM) methodology using an improved S-shaped membership function has been developed for finding out the "best candidate FMS alternative" from a set of candidate-FMSs. The MCDM model trade-offs among various parameters, viz., design parameters, economic considerations, etc., affecting the FMS selection process under multiple, conflicting-in-nature criteria environment. The selection of FMS is made according to the error output of the results found from the proposed MCDM model. 2007 Citation Index Journal NonPeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/127/1/paper.pdf P., Vasant and A., Bhattacharya and A., Abraham and C., Grosan (2007) Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker. [Citation Index Journal] http://www.scopus.com/inward/record.url?eid=2-s2.0-38049061772&partnerID=40&md5=828f2e8eaeb8da9b97e57e6d254e667f |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science P., Vasant A., Bhattacharya A., Abraham C., Grosan Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker |
| title | Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
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| title_full | Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
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| title_fullStr | Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
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| title_full_unstemmed | Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
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| title_short | Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
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| title_sort | evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker |
| topic | Q Science (General) QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/127/ http://scholars.utp.edu.my/id/eprint/127/ http://scholars.utp.edu.my/id/eprint/127/1/paper.pdf |