A Hybrid Algorithm for Facility Layout Problem of Mixed Model Assembly Line

  • Shi-jun Yang
  • Ling ZhaoEmail author
Conference paper


For solving the facility layout problem of mixed model assembly line (MMAL-FLP), the multiobjective model of MMAL-FLP was built for optimizing logistics and production efficiency according to characteristics of MMAL. For minimizing logistics cost and maximizing line balance as the index of objectives, the new hybrid algorithm named nondominated sorting genetic algorithm 2 with tabu search (NSGA2-TS) was proposed to solve this model. NSGA2-TS apply the powerful ability for local search of TS to settle the premature convergence matter of NSGA2. The practical case study proved the effectiveness and feasibility of MMAL-FLP model and the validity and stability of the NSGA-TS.


MMAL-FLP Multiobjective NSGA2-TS 


  1. 1.
    C.J. Hyun, Y. Kim, Y.K. Kim, A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines. Comput. Oper. Res. 25(7–8), 675–690 (1998)CrossRefGoogle Scholar
  2. 2.
    K. Deb, A. Pratap, S. Agarwal et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    M.G. Gong, L.C. Jiao, D.D. Yang et al., Research on evolutionary multi-objective optimization algorithms. J. Softw. 20(20), 271–289 (2009)CrossRefGoogle Scholar
  4. 4.
    S. Halelfadl, A.M. Adham, N. Mohd-Ghazali et al., Optimization of thermal performances and pressure drop of rectangular microchannel heat sink using aqueous carbon nanotubes based nanofluid. Appl. Therm. Eng. 62(2), 492–499 (2014)CrossRefGoogle Scholar
  5. 5.
    M. Delgado, M.P. Cuellar, M.C. Pegalajar, Multiobjective hybrid optimization and training of recurrent neural networks. IEEE Trans. Syst. Man & Cybern. Part B Cybern. Publ. IEEE Syst. Man & Cybern. Soc. 38(2), 381 (2008)CrossRefGoogle Scholar
  6. 6.
    C.M. Kwan, C.S. Chang., Timetable synchronization of mass rapid transit system using multiobjective evolutionary approach. IEEE Press (2008)Google Scholar
  7. 7.
    H.C.W. Lau, T.M. Chan, W.T. Tsui et al., A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem. Expert. Syst. Appl. Int. J. 36(4), 8255–8268 (2009)CrossRefGoogle Scholar
  8. 8.
    C. Zhang, W. Li, P. Jiang et al., Experimental investigation and multi-objective optimization approach for low-carbon milling operation of aluminum. ARCHIVE Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 1989–1996. 203–210 (2016)Google Scholar
  9. 9.
    Q. Liu, W. Cai, J. Shen et al., A speculative approach to spatialtemporal efficiency with multiobjective optimization in a heterogeneous cloud environment. Secur. & Commun. Netw. 9(17), 4002–4012 (2016)CrossRefGoogle Scholar
  10. 10.
    F. Glover, J.P. Kelly, M. Laguna, Genetic algorithms and tabu search: hybrids for optimization. Comput. Oper. Res. 22(1), 111–134 (1995)CrossRefGoogle Scholar
  11. 11.
    J.F. Bard, E. Dar-Elj, A. Shtub, An analytic framework for sequencing mixed model assembly lines. Int. J. Prod. Res. 30(1), 35–48 (1992)CrossRefGoogle Scholar
  12. 12.
    B.H. Ulutas, A.A. Islier, A clonal selection algorithm for dynamic facility layout problems. J. Manuf. Syst. 28(4), 123–131 (2009)CrossRefGoogle Scholar
  13. 13.
    C. Becker, A survey on problems and methods in generalized assembly line balancing. Eur. J. Oper. Res. 168(3), 694–715 (2006)CrossRefGoogle Scholar
  14. 14.
    J. Li, B. Yang, D. Zhang et al., Development of a multi-objective scheduling system for offshore projects based on hybrid non-dominated sorting genetic algorithm. Adv. Mech. Eng. 7(3) (2015). Scholar
  15. 15.
    S. Carcangiu, A. Fanni, A. Montisci, Multiobjective tabu search algorithms for optimal design of electromagnetic devices. IEEE Trans. Magn. 44(6), 970–973 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Mechanical and Electric EngineeringSoochow UniversitySuzhouChina

Personalised recommendations