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

• Published in: International journal of production research Vol. 43; no. 4; pp. 829 - 853
• Main Authors: Yasuda, K., Hu, L., Yin, Y.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis Group 15.02.2005; Washington, DC Taylor & Francis; Taylor & Francis LLC
• Subjects: Applied sciences; Cell formation; Cellular manufacturing; Design optimization; Exact sciences and technology; Flows in networks. Combinatorial problems; Genetic algorithms; Grouping genetic algorithm; Multi-objective optimization; Operational research and scientific management; Operational research. Management science; Studies; Cell formation; Grouping genetic algorithm; Genetic algorithm; Optimal solution; Multiobjective programming; Cellular manufacturing; Comparison test; Grouping; Multi-objective optimization; Optimization; Machine part
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540512331311859

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.