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

Arijit Bhattacharya, Ajith Abraham, Pandian Vasant, Crina Grosan

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46 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)131-140
Number of pages10
JournalInternational Journal of Innovative Computing, Information and Control
Issue number1
Publication statusPublished - Feb 2007
Externally publishedYes


  • Flexible manufacturing systems
  • Hybrid approach
  • Meta-learning
  • Multi criteria decision-making
  • Neural networks

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