An improved genetic algorithm for the machine-part cell formation problem

Cellular manufacturing system (CMS) deals with the set of procedures used by the company to drive the production facilities and to manage production efficiently by solving the technical and logistic problems encountered in the factory, and ensuring that products meet quality standards. The CMS is well thought-out as an efficient production approach to make batch manufacturing as efficient and productive as possible. The CMS relies on the theory of group technology (GT) for grouping dissimilar machines in machine cells and grouping parts into part families to take the benefit of their similarities in design and production process. It reduce total intercellular pass as well as to make the most of the number of operations within a machine cell. This paper presents a meta-heuristic genetic algorithm to resolve machine cell formation problem (CFP) in CMS and paying concentration on maximizing grouping efficacy (GC) by reducing outside elements and void elements in diagonal blocks. Computational work was carried out on 36 standard problems from the literature. The outcome confirms that the proposed meta-heuristic in terms of GC has shown to produce solutions are either enhanced or aggressive with other accessible algorithms.