TY  - JOUR
N2  - 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.
IS  - 1
ID  - scholars276
JF  - International Journal of Innovative Computing, Information and Control
A1  - Bhattacharya, A.
A1  - Abraham, A.
A1  - Vasant, P.
A1  - Grosan, C.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049061772&partnerID=40&md5=828f2e8eaeb8da9b97e57e6d254e667f
VL  - 3
Y1  - 2007///
N1  - cited By 46
TI  - Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker
SP  - 131
AV  - none
EP  - 140
PB  - IJICIC Editorial Office
SN  - 13494198
ER  -