Hosseini, A., Paydar, M., Mahdavi, I., Jouzdani, J. (2016). Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm. Journal of Optimization in Industrial Engineering, 9(20), 41-51. doi: 10.22094/joie.2016.249
Adib Hosseini; Mohammad Mahdi Paydar; Iraj Mahdavi; Javid Jouzdani. "Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm". Journal of Optimization in Industrial Engineering, 9, 20, 2016, 41-51. doi: 10.22094/joie.2016.249
Hosseini, A., Paydar, M., Mahdavi, I., Jouzdani, J. (2016). 'Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm', Journal of Optimization in Industrial Engineering, 9(20), pp. 41-51. doi: 10.22094/joie.2016.249
Hosseini, A., Paydar, M., Mahdavi, I., Jouzdani, J. Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm. Journal of Optimization in Industrial Engineering, 2016; 9(20): 41-51. doi: 10.22094/joie.2016.249
Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm
1Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
2Department of Industrial Engineering, Babol Noshirvani University of Technology
3PhD, Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Abstract
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.
Highlights
A bi-objective mathematical programming model is designed for a virtual cellular manufacturing system with alternative processing routes
Optimal routing of parts, the layout of machines and the assignment of cells to locations are obtained
The proposed model is solved utilizing multi-choice goal programming and a genetic algorithm for large-sized problem instances
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