Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker

Bhattacharya, Arijit ORCID: https://orcid.org/0000-0001-5698-297X, Abraham, Ajith, Vasant, Pandian and Grosan, Crina (2007) Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker. International Journal of Innovative Computing, Information and Control, 3 (1). pp. 131-140. ISSN 1349-4198

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	flexible manufacturing systems,hybrid approach,meta-learning,multi criteria decision-making,neural networks,software,theoretical computer science,information systems,computational theory and mathematics ,/dk/atira/pure/subjectarea/asjc/1700/1712
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	05 May 2020 00:06
Last Modified:	22 Oct 2022 06:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/74979
DOI:

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