SCHEDULING IN A DYNAMIC CELLULAR MANUFACTURING SYSTEM WITH CONNECTED PERIODS AND CONSIDERING THE POSSIBILITY OF MACHINES RELOCATION DURING THE

Today, changes in the volume and type of customer demand are a serious and signicant problem for manufacturing companies. To address this problem, new production systems, including the dynamic cellular manufacturing system, have provided some solutions. In this system, the layout of machines can be changed from one period to another according to changes in demand. On the other hand, in the problem of scheduling parts in the cellular manufacturing system, the relocation of machines is usually done between two periods. Still, no time is considered for this relocation, and it is necessary to consider this time to determine the completion time of parts exactly. This paper introduces an innovative mathematical model to address the scheduling challenges in a cellular manufacturing system with continuous periods. The proposed model allows for dynamic machine relocation and layout changes within each period while taking into account the associated time and cost factors involved in the movement process. The possibility of machine relocation during the period can increase the system's dynamics. In the proposed model, cell formation coincides with scheduling. Other features of the model include alternative processing routes and the existence of identical versions of a machine. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. Validation of the proposed model is performed in ve steps. The results of examining the features of the proposed model show that the model can eectively reduce completion time and other costs. Finally, to solve the model in larger sizes, two meta-heuristic algorithms of simulated annealing (SA) and genetic algorithm (GA) have been designed, and the obtained results have been compared with the results of CPLEX solver.