Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm
محورهای موضوعی : Design of Experiment
Adib Hosseini
1
(Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran)
Mohammad Mahdi Paydar
2
(Department of Industrial Engineering, Babol Noshirvani University of Technology)
Iraj Mahdavi
3
(Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran)
Javid Jouzdani
4
(PhD, Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
کلید واژه: Genetic Algorithm, Mathematical Programming, Virtual Cellular Manufacturing, Multi-Choice Goal Programming,
چکیده مقاله :
Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.
