%0 Journal Article
%@ 13494198
%A Bhattacharya, A.
%A Abraham, A.
%A Vasant, P.
%A Grosan, C.
%D 2007
%F scholars:276
%I IJICIC Editorial Office
%J International Journal of Innovative Computing, Information and Control
%N 1
%P 131-140
%T Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
%U https://khub.utp.edu.my/scholars/276/
%V 3
%X 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.
%Z cited By 46