Dynamic Cellular Manufacturing Systems and Their Solution Using Genetic Algorithm

Production planning and cell formation problems are two important parts of this system, which have mutual effects. In this work, a comprehensive, non-linear mathematical model with integer variables has been proposed for such problems for cell formation and production planning in a dynamic cellular manufacturing system. The proposed model is aimed at reduction of the expenses associated with production planning under dynamic conditions and the costs related to the construction and formation of cells including the costs of cell preparation and activation. The expenses investigated in this research include the costs of buying machinery, machine operation, intercellular material transfer, cell reconfiguration, activation of cells, maintenance of an inventory of assurance, maintenance of an end of the period inventory and part production as well as the unpredictable, variable costs of cell activation and buying machinery. Since the proposed model is an NP-Hard model, an effective genetic algorithm has been used. In order to evaluate and confirm the performance of the algorithms used, the results have been compared with those obtained from GAM software for different problems from the perspectives of response quality and solution time.