Using Ant Colony Meta-heuristic Approach for Cellular Manufacturing System

Grouping the machines and parts in a manufacturing system, based on similarities is known as cell formation problem. The problem is clustering items to form identified and specific groups having similar properties. Cellular Manufacturing System (CMS) can be defined as an application of Group Technology (GT) philosophy that allows decomposing a manufacturing system into subsystems to make it easier to manage than the entire manufacturing system. It has been shown that the grouping algorithm is NP-hard problem and the mathematical model is non-linear program. In fact, for real manufacturing systems with a large number of machines and parts, it may take a very long time to solve the problem using mathematical methods. That is why the heuristic approaches have attained the attention of many researchers and academicians. In this paper, we use Ant Colony Optimization (ACO) method as an evolutionary approach to solve the cell formation problems. This model uses a P = [Pij] (C)×(M+P) pheromone matrix in which, C, M, and P, are the number of cells, machines and parts respectively. The algorithm is programmed in the C language, and it is tested for some familiar examples mentioned in CMS literatures to show the applicability of the proposed model.