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

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]

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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:Citation Index Journal
Uncontrolled Keywords:Flexible manufacturing systems; Hybrid approach; Meta-learning; Multi criteria decision-making; Neural networks
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:127
Deposited By: Mr Helmi Iskandar Suito
Deposited On:02 Mar 2010 01:18
Last Modified:19 Jan 2017 08:27

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