Skip to main content
Log in

Pareto-optimal front of cell formation problem in group technology

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

The earliest approaches to the cell formation problem in group technology, dealing with a binary machine-part incidence matrix, were aimed only at minimizing the number of intercell moves (exceptional elements in the block-diagonalized matrix). Later on this goal was extended to simultaneous minimization of the numbers of exceptions and voids, and minimization of intercell moves and within-cell load variation, respectively. In this paper we design the first exact branch-and-bound algorithm to create a Pareto-optimal front for the bi-criterion cell formation problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Arkat, J., Hosseini, L., Farahani, M.H.: Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Syst. Appl. 38(8), 9597–9602 (2011). doi:10.1016/j.eswa.2011.01.161

    Article  Google Scholar 

  2. Bajestani, M.A., Rabbani, M., Rahimi-Vahed, A., Khoshkhou, G.B.: A multi-objective scatter search for a dynamic cell formation problem. Comput. Oper. Res. 36(3), 777–794 (2009). doi:10.1016/j.cor.2007.10.026

    Article  MATH  Google Scholar 

  3. Batsyn, M., Bychkov, I., Goldengorin, B., Pardalos, P.M., Sukhov, P.: Pattern-based heuristic for the cell formation problem in group technology. In: B. Goldengorin, V.A. Kalyagin, P.M. Pardalos (eds.) Models, Algorithms, and Technologies for Network Analysis, Springer Proceedings in Mathematics & Statistics, vol. 32, pp. 11–50. Springer, New York (2013). doi:10.1007/978-1-4614-5574-52

  4. Boulif, M., Atif, K.: A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies. Int. J. Approx. Reason. 47(2), 141–165 (2008). doi:10.1016/j.ijar.2007.03.003

    Article  MATH  MathSciNet  Google Scholar 

  5. Chinchuluun, A., Pardalos, P.M.: A survey of recent developments in multiobjective optimization. Ann. Oper. Res. 154, 29–50 (2007). doi:10.1007/s10479-007-0186-0

    Article  MATH  MathSciNet  Google Scholar 

  6. Dimopoulos, C.: A review of evolutionary multiobjective optimization applications in the area of production research. In: Congress on Evolutionary Computation (CEC2004), vol. 2, pp. 1487–1494 (2004). doi:10.1109/CEC.2004.1331072

  7. Dimopoulos, C.: Explicit consideration of multiple objectives in cellular manufacturing. Eng. Optim. 39(5), 551–565 (2007). doi:10.1080/03052150701351631

    Article  Google Scholar 

  8. Fontes, D.B.M.M., Gaspar-Cunha, A.: On multi-objective evolutionary algorithms. In: C. Zopounidis, P.M. Pardalos (eds.) Handbook of Multicriteria Analysis, Applied Optimization, vol. 103, pp. 287–310. Springer, Berlin (2010). doi:10.1007/978-3-540-92828-7_10

  9. Goldengorin, B., Krushinsky, D., Pardalos, P.M.: Cell Formation in Industrial Engineering: Theory, Algorithms and Experiments. Springer, New York (2013)

    Book  Google Scholar 

  10. Goldengorin, B., Krushinsky, D., Slomp, J.: Flexible PMP approach for large-size cell formation. Oper. Res. 60(5), 1157–1166 (2012). doi:10.1287/opre1120.1108

    Article  MATH  MathSciNet  Google Scholar 

  11. Goldengorin, B., Pardalos, P.M.: Data Correcting Approaches in Combinatorial Optimization. Springer, New York (2012)

    Book  MATH  Google Scholar 

  12. Lee, S.D., Chen, Y.L.: A weighted approach for cellular manufacturing design: minimizing intercell movement and balancing workload among duplicated machines. Int. J. Prod. Res. 35(4), 1125–1146 (1997). doi:10.1080/002075497195588

    Article  MATH  MathSciNet  Google Scholar 

  13. Lei, D., Wu, Z.: Tabu search for multiple-criteria manufacturing cell design. Int. J. Adv. Manuf. Technol. 28, 950–956 (2006). doi:10.1007/s00170-004-2441-8

    Article  Google Scholar 

  14. Malakooti, B., Yang, Z.: Multiple criteria approach and generation of efficient alternatives for machine-part family formationin group technology. IIE Trans. 34, 837–846 (2002). doi:10.1023/A:1015557007084

    Google Scholar 

  15. Mansouri, S.A., Husseini, S.M., Newman, S.: A review of the modern approaches to multi-criteria cell design. Int. J. Prod. Res. 38(5), 1201–1218 (2000). doi:10.1080/002075400189095

    Article  MATH  Google Scholar 

  16. Neto, A.R.P., Filho, E.V.G.: A simulation-based evolutionary multiobjective approach to manufacturing cell formation. Comput. Ind. Eng. 59(1), 64–74 (2010). doi:10.1016/j.cie.2010.02.017

    Article  Google Scholar 

  17. Papaioannou, G., Wilson, J.M.: The evolution of cell formation problem methodologies based on recent studies (1997–2008): review and directions for future research. Eur. J. Oper. Res. 206(3), 509–521 (2010). doi:10.1016/j.ejor.2009.10.020

    Article  MATH  Google Scholar 

  18. Paulavičius, R., Žilinskas, J., Grothey, A.: Investigation of selection strategies in branch and bound algorithm with simplicial partitions and combination of Lipschitz bounds. Optim. Lett. 4, 173–183 (2010). doi:10.1007/s11590-009-0156-3

    Article  MATH  MathSciNet  Google Scholar 

  19. Saaty, T.L.: The modern science of multicriteria decision making and its practical applications: the AHP/ANP approach. Oper. Res. 61(5), 1101–1118 (2013). doi:10.1287/opre2013.1197

    Article  MATH  MathSciNet  Google Scholar 

  20. Su, C.T., Hsu, C.M.: Multi-objective machine-part cell formation through parallel simulated annealing. Int. J. Prod. Res. 36(8), 2185–2207 (1998). doi:10.1080/002075498192841

    Article  MATH  Google Scholar 

  21. Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G., Siadat, A.: A cell formation problem considering machine utilization and alternative process routes by scatter search. J. Intell. Manuf. 23, 1127–1139 (2012). doi:10.1007/s10845-010-0395-2

    Article  Google Scholar 

  22. 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). doi:10.1016/0360-8352(92)90022-C

    Article  Google Scholar 

  23. Wemmerlov, U., Johnson, D.J.: Empirical findings on manufacturing cell design. Int. J. Prod. Res. 38(3), 481–507 (2000). doi:10.1080/002075400189275

    Article  Google Scholar 

  24. Zopounidis, C., Pardalos, P.M. (eds.): Handbook of Multicriteria Analysis, Applied Optimization, vol. 103. Springer, Berlin (2010). doi:10.1007/978-3-540-92828-7

  25. Žilinskas, A., Žilinskas, J.: Branch and bound algorithm for multidimensional scaling with city-block metric. J. Glob. Optim. 43, 357–372 (2009). doi:10.1007/s10898-008-9306-x

    Article  MATH  Google Scholar 

  26. Žilinskas, J.: Reducing of search space of multidimensional scaling problems with data exposing symmetries. Inf. Technol. Control 36(4), 377–382 (2007)

    Google Scholar 

  27. Žilinskas, J.: Branch and bound with simplicial partitions for global optimization. Math. Model. Anal. 13(1), 145–159 (2008). doi:10.3846/1392-6292.2008.13.145-159

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research was funded by a Grant (No. MIP-063/2012) from the Research Council of Lithuania. P. M. Pardalos is partially supported by LATNA Laboratory, NRU HSE, RF Government Grant, ag. 11.G34.31.0057. We thank D. Krushinsky for a fruitful discussion.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julius Žilinskas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Žilinskas, J., Goldengorin, B. & Pardalos, P.M. Pareto-optimal front of cell formation problem in group technology. J Glob Optim 61, 91–108 (2015). https://doi.org/10.1007/s10898-014-0154-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-014-0154-6

Keywords