Interactive optimization strategies for layout problems

  • Julien Bénabès
  • Fouad Bennis
  • Emilie Poirson
  • Yannick Ravaut
Original Paper


Component and facility layout plays an important role in the design and usability of many engineering products and systems as mechanical design, process plan, management and architecture including ship compartment layout,... Because of the great complexity of most industrial layout problems, the decision of the acceptable layout is a hard and critical task since the special layout can have a significant consequence on the user satisfaction, the economic cost and broadly speaking the global performances. Thus, in order to propose to the designer an optimal spatial arrangement in a reasonable time, this paper develops an interactive optimization strategy based on a genetic algorithm coupled with a separation algorithm. The proposed method is tested on the layout problem of a shelter. The resolution of this problem is innovative because it introduces the concept of space of accessibility in the layout problem formulation.


Layout problem Interactive optimization Genetic algorithm 


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  1. 1.
    Brintrup A.M., Ramsden J., Tiwari A.: An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. Comput. Ind. 58, 279–291 (2007)CrossRefGoogle Scholar
  2. 2.
    Cagan J., Shimada K., Yin S.: A survey of computational approaches to the three-dimensional layout problems. Comput. Aided Design 34, 597–611 (2002)CrossRefGoogle Scholar
  3. 3.
    Deb K.: Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol. Comput. 7, 205–230 (1998)CrossRefGoogle Scholar
  4. 4.
    Drira A., Pierreval H., Hajri-Gabouj S.: Facility layout problems: a survey. Ann. Rev. Control 31, 255–267 (2007)Google Scholar
  5. 5.
    Dyckhoff H.: A typology of cutting and packing problems. Eur. J. Oper. Res. 44(2), 145–159 (1990)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Giassi A., Bennis F., Maisonneuve J.J.: Multidisciplinary design optimisation and robust design approaches applied to concurrent design. Struct. Multidiscip. Optim. 28, 356–371 (2004)CrossRefGoogle Scholar
  7. 7.
    Imamichi, T., Nagamochi, H.: A multi-sphere scheme for 2d and 3d packing problems. In: SLS 2007: Proceedings of engineering stochastic local search algorithms. designing, implementing and analyzing effective heuristics. Lecture Notes in Computer Science, vol. 4638, pp. 207–211. Springer, Heidelberg. doi:10.1007/978-3-540-74446-7_19 (2007)
  8. 8.
    Imamichi, T., Nagamochi, H.: Designing algorithms with multi-sphere scheme. In: ICKS ’08: Proceedings of the International Conference on Informatics Education and Research for Knowledge-Circulating Society (icks 2008), pp. 125–130. IEEE Computer Society, Washington, DC, doi:10.1109/ICKS.2008.22 (2008)
  9. 9.
    Michalek J.J., Papalambros P.Y.: Interactive design optimization of architectural layouts. Eng. Optim. 34, 485–501 (2002)CrossRefGoogle Scholar
  10. 10.
    Poirson E., Petiot J.F., Gilbert J.: Integration of user perceptions in the design process: application to musical instrument optimization. J. Mech. Design 129, 1206–1214 (2007)CrossRefGoogle Scholar
  11. 11.
    Poles, S.: Moga-ii, an improved multi-objective genetic algorithm. ESTECO, Technical Report 2003-006 p 1/16 (2003)Google Scholar
  12. 12.
    Rabeau S., Depince P., Bennis F.: Collaborative optimization of complex systems: a multidisciplinary approach. Int. J. Interact. Design Manuf. 1, 209–218 (2007)CrossRefGoogle Scholar
  13. 13.
    Serna, L., Mejia, R., Bennis, F., Fischer, X.: Some ways of implementation of interactive design. In: Virtuel Concept 2005, Biarritz (2005)Google Scholar
  14. 14.
    Su Y., Cagan J.: An extended pattern search algorithm for threedimensional component layout. J. Mech. Design 122, 102–108 (2000)CrossRefGoogle Scholar
  15. 15.
    Szykman S., Cagan J.: Constrained three-dimensional component layout using simulated annealing. J. Mech. Design 119, 28–35 (1997)CrossRefGoogle Scholar
  16. 16.
    Yi M., Fadel G.M., Gantovnik V.B.: Vehicle configuration design with a packing genetic algorithm. Int. J. Heavy Veh. Syst. 15, 433–448 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Julien Bénabès
    • 1
  • Fouad Bennis
    • 1
  • Emilie Poirson
    • 1
  • Yannick Ravaut
    • 2
  1. 1.Ecole Centrale de Nantes, Irccyn, UMR CNRS 6597NantesFrance
  2. 2.Thales CommunicationsCholetFrance

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