Meta-Learning Evolutionary Artificial Neural Network for Selecting Flexible Manufacturing Systems
This paper proposes the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) in selecting flexible manufacturing systems (FMS) from a group of candidate FMS’s. First, multi-criteria decisionmaking (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, namely, design parameters, economic considerations, etc., affecting the FMS selection process in multi-criteria decision-making environment. Genetic algorithm is used to evolve the architecture and weights of the proposed neural network method. Further, a back-propagation (BP) algorithm is used as the local search algorithm. The selection of FMS is made according to the error output of the results found from the MCDM model.
KeywordsFlexible Manufacturing System Local Search Algorithm Analytical Hierarchical Process Reconfigurable Manufacture System Linear Genetic Programming
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