Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Single linkage versus average linkage clustering in machine cells formation applications
Computers and Industrial Engineering
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
International Journal of Approximate Reasoning
Computers and Operations Research
A spectral clustering algorithm for manufacturing cell formation
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Applying simulated annealing for designing cellular manufacturing systems using MDmTSP
Computers and Industrial Engineering
Genetic approaches for graph partitioning: a survey
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Group technology based adaptive cell formation using predator-prey genetic algorithm
Applied Soft Computing
Cell formation in group technology using constraint programming and Boolean satisfiability
Expert Systems with Applications: An International Journal
Solving manufacturing cell design problems using constraint programming
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Computers and Industrial Engineering
Computers and Operations Research
Computers and Industrial Engineering
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This paper deals with the manufacturing cell formation (MCF) problem, which is based on group technology principles, using a graph partitioning formulation. An attempt has been made to take into account the natural constraints of real-life production systems, such as operation sequences, minimum and maximum numbers of cells, and maximum cell sizes. Cohabitation constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and non-cohabitation constraints were included to prevent placing certain machines in close vicinity.First, the problem is solved with a genetic algorithm (GA), using a binary coding system that has proved superior to the classic integer coding systems. A new Branch-and-Bound (B&B) enhancement is then proposed to improve the GA's performance. The results obtained for medium-sized instances using this enhancement are better than those obtained using the GA alone. Given these results, it is reasonable to assume that the B&B enhancement will provide good results for large real-life problems.