نوع مقاله : پژوهشی
1 استادیار گروه مهندسی صنایع، دانشگاه صنعتی بیرجند
2 استاد دانشکده مهندسی صنایع، دانشگاه یزد
3 دانشیار دانشکده مهندسی صنایع، دانشگاه یزد
عنوان مقاله [English]
Today, changes in the volume and type of demand from customers is a serious and important problem for manufacturing companies. To address this problem, new production systems, including the dynamic cellular manufacturing system, have been able to provide 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 of parts in the cellular manufacturing system, the relocation of machines is usually done between two periods, but no time is considered for this relocation, while without considering this time, it is not possible to determine the completion time of parts exactly. To solve this problem, this paper presents a new mathematical model for the scheduling problem in a cellular manufacturing system in which periods are continuous and machines relocation and changing the layout can be done during the period by considering the time and cost of moving. The possibility of machine relocation during the period can increase the dynamics of the system. In the proposed model, cell formation occurs simultaneously 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 five steps. The results of examining the features of the proposed model show that the model can be effective in reducing completion time and reducing 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 results of CPLEX solver.