Cell formation in group technology: review, evaluation and directions for future research
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An asynchronous parallel metaheuristic for the period vehicle routing problem
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
An evolutionary algorithm for manufacturing cell formation
Computers and Industrial Engineering
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A simulated annealing algorithm for manufacturing cell formation problems
Expert Systems with Applications: An International Journal
A linear assignment clustering algorithm based on the least similar cluster representatives
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Computers and Operations Research
A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain
Computers and Industrial Engineering
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The cell formation problem is a crucial component of a cell production design in a manufacturing system. This problem consists of a set of product parts to be manufactured in a group of machines. The objective is to build manufacturing clusters by associating part families with machine cells, with the aim of minimizing the inter-cellular movements of parts by grouping efficacy measures. We present two approaches to solve the cell formation problem. First, we present an evolutionary algorithm that improves the efficiency of the standard genetic algorithm by considering cooperation with a local search around some of the solutions it visits. Second, we present an approach based on simulated annealing that uses the same representation scheme of a feasible solution. To evaluate the performance of both algorithms, we used a known set of CFP instances. We compared the results of both algorithms with the results of five other algorithms from the literature. In eight out of 36 instances we considered, the evolutionary method outperformed the previous results of other evolutionary algorithms, and in 26 instances it found the same best solutions. On the other hand, simulated annealing not only found the best previously known solutions, but it also found better solutions than existing ones for various problems.