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Grouping genetic algorithms: an efficient method to solve the cell formation problem

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  • De Lit, P.
  • Falkenauer, E.
  • Delchambre, A.

Abstract

The layout problem arises in a production plant during the study of a new production system, but also during a possible restructuring. The main aim of layout design is to reduce transportation and maintenance, which simplifies management, shortens lead time, improves product quality and speeds up the response to market fluctuations. A principle of Group Technology (GT) advocates the division of a unity into small groups or cells. As it is most of the time impossible to design totally independent cells, the problem is to minimise traffic of items between the cells, for a fixed maximum cell size. This problem is known as cell formation problem (CFP). We propose here an original approach to solve this NP-hard problem. It is based on a Grouping Genetic Algorithm (GGA), a special class of genetic algorithms, heavily modified to suit the structure of grouping problems. The crucial advantage of this GGA is that it is able to deal with large instances of the problem thus becoming a powerful tool for an engineer determining a plant layout, allowing him or her to try several plant options, without the limitation of huge computation times.

Suggested Citation

  • De Lit, P. & Falkenauer, E. & Delchambre, A., 2000. "Grouping genetic algorithms: an efficient method to solve the cell formation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(3), pages 257-271.
    • Handle: RePEc:eee:matcom:v:51:y:2000:i:3:p:257-271
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    Cited by:

    1. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    2. Manoj Kumar Paras & Lichuan Wang & Yan Chen & Antonela Curteza & Rudrajeet Pal & Daniel Ekwall, 2018. "A Sustainable Application Based on Grouping Genetic Algorithm for Modularized Redesign Model in Apparel Reverse Supply Chain," Sustainability, MDPI, vol. 10(9), pages 1-19, August.
    3. Vila Goncalves Filho, Eduardo & Jose Tiberti, Alexandre, 2006. "A group genetic algorithm for the machine cell formation problem," International Journal of Production Economics, Elsevier, vol. 102(1), pages 1-21, July.
    4. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.

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