@article{scholars276,
          number = {1},
            note = {cited By 46},
          volume = {3},
           title = {Evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker},
            year = {2007},
           pages = {131--140},
         journal = {International Journal of Innovative Computing, Information and Control},
       publisher = {IJICIC Editorial Office},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049061772&partnerID=40&md5=828f2e8eaeb8da9b97e57e6d254e667f},
        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.},
            issn = {13494198},
          author = {Bhattacharya, A. and Abraham, A. and Vasant, P. and Grosan, C.}
}