A grouping genetic algorithm for the multi-objective cell formation problem

K. Yasuda, L. Hu, Y. Yin

Research output: Contribution to journalArticlepeer-review

78 Citations (Scopus)


In this research, we propose an efficient method to solve the multi-objective cell formation problem (CFP) partially adopting Falkenauer's grouping genetic algorithm (GGA). The objectives are the minimization of both the cell load variation and intercell flows considering the machines' capacities, part volumes and part processing times on the machines. We relax the cell size constraints and solve the CFP without predetermination of the number of cells, which is usually difficult to predict in a real-world CFP design. We also make some effort to improve the efficiency of our algorithm with respect to initialization of the population, fitness valuation, and keeping crossover operator from cloning. Numerical examples are tested and comparisons are made with general genetic algorithms (GAs). The result shows that our method is effective and flexible in both grouping machines into cells and deciding on the number of cells for the optimal solution.

Original languageEnglish
Pages (from-to)829-853
Number of pages25
JournalInternational Journal of Production Research
Issue number4
Publication statusPublished - 2005 Feb 15


  • Cell formation
  • Cellular manufacturing
  • Grouping genetic algorithm
  • Multi-objective optimization

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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