A new cut-based genetic algorithm for graph partitioning applied to cell formation

TL;DR

This paper introduces a novel cut-based genetic algorithm for graph partitioning to improve cell formation in manufacturing systems, considering real-world constraints and demonstrating promising results on medium-sized instances.

Contribution

It presents an original encoding for a genetic algorithm tailored to graph partitioning in cell formation, incorporating real-life production constraints.

Findings

The new GA outperforms some existing methods on medium-sized instances.

Results suggest the approach is scalable to larger real-world problems.

The method effectively handles constraints like operation sequences and cell sizes.

Abstract

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.