CF-GGA: A grouping genetic algorithm for the cell formation problem. In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identification of machine groups based on their ability to process specific part families. Past research has shown that one key aspect of attaining efficient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGA’s solutions using the measurements of efficiency [M. P. Chandrasekharan and R. Rajagopalan, Int. J. Prod. Res. 24, 451–463 (1986; Zbl 0582.90050)] and efficacy (Kumar and Chandrasekharan 1990). The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Bychkov, Ilya; Batsyn, Mikhail: An efficient exact model for the cell formation problem with a variable number of production cells (2018)
  2. Husseinzadeh Kashan, Ali; Husseinzadeh Kashan, Mina; Karimiyan, Somayyeh: A particle swarm optimizer for grouping problems (2013)
  3. Landa-Torres, I.; Del Ser, J.; Salcedo-Sanz, Sancho; Gil-Lopez, S.; Portilla-Figueras, J. A.; Alonso-Garrido, O.: A comparative study of two hybrid grouping evolutionary techniques for the capacitated P-median problem (2012)
  4. Agustín-Blas, Luis E.; Salcedo-Sanz, Sancho; Ortiz-García, Emilio G.; Portilla-Figueras, Antonio; Pérez-Bellido, Ángel M.; Jiménez-Fernández, Silvia: Team formation based on group technology: a hybrid grouping genetic algorithm approach (2011)
  5. Bhatnagar, R.; Saddikuti, V.: Models for cellular manufacturing systems design: matching processing requirements and operator capabilities (2010)
  6. Li, Xiangyong; Baki, M. F.; Aneja, Y. P.: An ant colony optimization metaheuristic for machine-part cell formation problems (2010)
  7. Tavakkoli-Moghaddam, R.; Rahimi-Vahed, A. R.; Ghodratnama, A.; Siadat, A.: A simulated annealing method for solving a new mathematical model of a multi-criteria cell formation problem with capital constraints (2009)
  8. Vitanov, V.; Tjahjono, B.; Marghalany, I.: Heuristic rules-based logic cell formation algorithm (2008)
  9. Brown, Evelyn C.; Ragsdale, Cliff T.; Carter, Arthur E.: A grouping genetic algorithm for the multiple traveling salesperson problem (2007)
  10. James, Tabitha L.; Brown, Evelyn C.; Keeling, Kellie B.: A hybrid grouping genetic algorithm for the cell formation problem (2007)
  11. Hu, L.; Yasuda, K.: Minimising material handling cost in cell formation with alternative processing routes by grouping genetic algorithm (2006)
  12. Chaudhry, S. S.; Luo, W.: Application of genetic algorithms in production and operations management: a review (2005)
  13. Lei, D.; Wu, Z.: Tabu search approach based on a similarity coefficient for cell formation in generalized group technology (2005)
  14. Rogers, David F.; Kulkarni, Shailesh S.: Optimal bivariate clustering and a genetic algorithm with an application in cellular manufacturing (2005)
  15. Brown, Evelyn C.; Sumichrast, Robert T.: Impact of the replacement heuristic in a grouping genetic algorithm. (2003)
  16. Brown, Evelyn C.; Sumichrast, Robert T.: CF-GGA: A grouping genetic algorithm for the cell formation problem (2001)