A new cut-based genetic algorithm for graph partitioning applied to cell formation
This work addresses cell formation for manufacturing system design, but it appears incremental as it builds on existing cut-based graph partitioning models with a new encoding.
The authors tackled the cell formation problem in manufacturing by proposing a new genetic algorithm with an original encoding representation, achieving competitive results on medium-sized instances and suggesting scalability to large real-life problems.
Cell formation is a critical step in the design of cellular manufacturing systems. Recently, it was tackled using a cut-based-graph-partitioning model. This model meets real-life production systems requirements as it uses the actual amount of product flows, it looks for the suitable number of cells, and it takes into account the natural constraints such as operation sequences, maximum cell size, cohabitation and non-cohabitation constraints. Based on this model, we propose an original encoding representation to solve the problem by using a genetic algorithm. We discuss the performance of this new GA in comparison to some approaches taken from the literature on a set of medium sized instances. Given the results we obtained, it is reasonable to assume that the new GA will provide similar results for large real-life problems. Keywords: Group Technology, Manufacturing Cell Formation, Graph Partitioning, Graph Cuts, Genetic Algorithm, Encoding representation.