A genetic algorithm approach to the machine-component grouping problem with multiple objectives
Computers and Industrial Engineering
A genetic algorithm approach to cellular manufacturing systems
Computers and Industrial Engineering
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
A meta-heuristic approach for cell formation problem
Proceedings of the Second Symposium on Information and Communication Technology
Computers and Industrial Engineering
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This paper presents a fuzzy linear mix-integer programming model for design of cellular manufacturing systems with fuzzy part demands and product mix changeable under a multi-period planning horizon. In this dynamic condition, the best cell design for one period may not be efficient for subsequent periods and the reconfiguration of cells is required. The proposed model can determine the production volume for each part considering its fuzzy demand. The other advantages of the proposed model are as follows: considering inter-cell material handling with constant batch size, alternative process plan for part types, operation sequence, machine relocation, machine replication, machine utilization and cell number flexibility. Main constraints are the cell size, machine capacity and production volume limitations. The objective is to minimize the sum of the constant/variable/relocation machine costs as well as inter-cell movements cost. Because of the complexity of the proposed model, which is a combinatorial nonlinear optimization, we develop an efficient genetic algorithm with novel representation and operators for solving the proposed model. 29 small, medium and large-sized problems are generated to evaluate the performance of the proposed model and the efficiency of the developed genetic algorithm.