Abstract
Cellular manufacturing system (CMS) is an important application in group technology. CMS design involves identifying part families and machine groups. Genetic algoritllm (GA) is a robust adaptive optimization method based on principles of natural evolution, having the advantages of parallel processing and multi-point searching. On the other hand, neural networks can be used to identify similar patterns at a high computational rate. Therefore, GA search method and neural network method are well suited for the machine-component grouping (MCG) problem. In this work, a mathematical programming formulation is presented which considers the intercell part flow and the manufacturing cell density for the MeG problem. Two algorithms based on GA and neural networks for solving the problem are also proposed. Implementation results demonstrate that both proposed algorithms can provide feasible cell grouping solutions under machine capacity constraints. These results also demonstrate that GA can perform adequate tasks when the fitness function is carefully selected. On the other hand, the proposed modified ART-1 model can also achieve a good performance when the appropriate vigilance parameter is given. © 1998 Taylor & Francis Group, LLC.