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Solving the Manufacturing Cell Design Problem via Invasive Weed Optimization

  • Ricardo SotoEmail author
  • Broderick Crawford
  • Carlos Castillo
  • Fernando Paredes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)

Abstract

Manufacturing plants are commonly organized in cells containing machines that process different parts of a given product. The Manufacturing Cell Design Problem (MCDP) aims at efficiently organizing the machines into cells in order to increase productivity by minimizing the inter-cell moves of parts. In this paper, we present a new approach based on Invasive Weed Optimization (IWO) for solving such a problem. The IWO algorithm is a recent metaheuristic inspired on the colonization behavior of the invasive weeds in agriculture. IWO represents the solutions as weeds that grow and produce seeds to be randomly dispersed over the search area. We additionally incorporate a binary neighbor operator in order to efficiently handle the binary nature of the problem. The experimental results demonstrate the efficiency of the proposed approach which is able to reach several global optimums for a set of 90 well-known MCDP instances.

Keywords

Manufacturing Cell Design Invasive Weed Optimization Metaheuristics Optimization 

References

  1. 1.
    Aljaber, N., Baek, W., Chen, C.: A tabu search approach to the cell formation problem. Comput. Ind. Eng. 32(1), 169–185 (1997)CrossRefGoogle Scholar
  2. 2.
    Boctor, F.F.: A linear formulation of the machine-part cell formation problem. Int. J. Prod. Res. 29(2), 343–356 (1991)CrossRefGoogle Scholar
  3. 3.
    Boulif, M., Atif, K.: A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem. Comput. Oper. Res. 33, 2219–2245 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Durán, O., Rodriguez, N., Consalter, L.: Collaborative particle swarm optimization with a data mining technique for manufacturing cell design. Expert Syst. Appl. 37(2), 1563–1567 (2010)CrossRefGoogle Scholar
  5. 5.
    James, T., Brown, E., Keeling, K.: A hybrid grouping genetic algorithm for the cell formation problem. Comput. Oper. Res. 34(7), 2059–2079 (2007)CrossRefzbMATHGoogle Scholar
  6. 6.
    Kusiak, A., Chow, W.: Efficient solving of the group technology problem. J. Manuf. Syst. 6, 117–124 (1987)CrossRefGoogle Scholar
  7. 7.
    Lenin, I., Reddy, B.R., Kalavathi, M.S.: Hybrid-invasive weed optimization particle swarm optimization algorithm for solving optimal reactive power dispatch problem. Int. J. Res. Electron. Commun. Technol. (IJRECT 2014), 1(1), 41–45 (2014)Google Scholar
  8. 8.
    Lozano, S., Díaz, A., Eguía, I., Onieva, L.: A one-step tabu search algorithm for manufacturing cell design. J. Oper. Res. Soc. 50(5) (1999)Google Scholar
  9. 9.
    Mallahzadeh, A.R.R., Oraizi, H., Davoodi-Rad, Z.: Application of the invasive weed optimization technique for antenna configurations. Prog. Electromagnetics Res. 79, 137–150 (2008)CrossRefGoogle Scholar
  10. 10.
    Medina, P.D., Cruz, E.A., Pinzon, M.: Generacion de celdas de manufactura usando el algoritmo de ordenamiento binario (aob). Scientia et Technica Ao XVI 16(44), 106–110 (2010)Google Scholar
  11. 11.
    Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1(4), 355–366 (2006)CrossRefGoogle Scholar
  12. 12.
    Mururgan, M., Selladurai, V.: Manufacturing cell design with reduction in setup time through genetic algorithm. J. Theor. Appl. Inf. Technol. 3(1), 76–97 (2006)Google Scholar
  13. 13.
    Nsakanda, A., Diaby, M., Price, W.: Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings. Eur. J. Oper. Res. 171(3), 1051–1070 (2006)CrossRefzbMATHGoogle Scholar
  14. 14.
    Olivia-Lopez, E., Purcheck, G.: Load balancing for group technology planning and control. Int. J. MTDR 19, 259–268 (1979)Google Scholar
  15. 15.
    Purcheck, G.: A linear—programming method for the combinatorial grouping of an incomplete set. J. Cybern. 5, 51–58 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Soto, R., Kjellerstrand, H., Durán, O., Crawford, B., Monfroy, E., Paredes, F.: Cell formation in group technology using constraint programming and boolean satisfiability. Expert Syst. Appl. 39(13), 11423–11427 (2012)CrossRefGoogle Scholar
  17. 17.
    Veenhuis, C.: Binary invasive weed optimization. Second World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 449–454 (2010)Google Scholar
  18. 18.
    Venugopal, V., Narendran, T.: A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Comput. Ind. Eng. 22(4), 469–480 (1992)CrossRefGoogle Scholar
  19. 19.
    Wu, T., Chang, C., Chung, S.: A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst. Appl. 34(3), 1609–1617 (2008)CrossRefGoogle Scholar
  20. 20.
    Xambre, A.R., Vilarinho, P.M.: A simulated annealing approach for manufacturing cell formation with multiple identical machines. Eur. J. Oper. Res. 151(2), 434–446 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Yin, Y., Yasuda, K.: Manufacturing cells design in consideration of various production. Int. J. Prod. Res. 40(4), 885–906 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ricardo Soto
    • 1
    • 2
    • 3
    Email author
  • Broderick Crawford
    • 1
    • 4
    • 5
  • Carlos Castillo
    • 1
  • Fernando Paredes
    • 6
  1. 1.Pontificia Universidad Católica de ValparaísoValparaísoChile
  2. 2.Universidad Autónoma de ChileSantiagoChile
  3. 3.Universidad Cientifica del SurLimaPeru
  4. 4.Universidad Central de ChileSantiagoChile
  5. 5.Universidad San SebastiánSantiagoChile
  6. 6.Escuela de Ingeniería IndustrialUniversidad Diego PortalesSantiagoChile

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