An Ordinal Regression Approach for the Unequal Area Facility Layout Problem

  • M. Pérez-OrtizEmail author
  • L. García-Hernández
  • L. Salas-Morera
  • A. Arauzo-Azofra
  • C. Hervás-Martínez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)


This paper proposes the use of ordinal regression for helping the evaluation of Unequal Area Facility Layouts generated by an interactive genetic algorithm. Using this approach, a model obtained taking into account some objective factors and the subjective evaluation of the experts is constructed. Ordinal regression is used in this case because of the ordinal ranking between the different possible evaluations of the facility layouts made by the experts: {very deficient, deficient, intermediate, good, very good}. To do so, we will also make an approximation to some of the most successful ordinal classification methods in the machine learning literature. The best model obtained will be used in order to guide the searching of a genetic algorithm for generating new facility layouts.


Ordinal Regression Mean Absolute Error Facility Layout Facility Layout Problem Plant Layout 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aiello, G., Enea, M.: Fuzzy approach to the robust facility layout in uncertain production environments. International Journal of Production Research 39(18), 4089–4101 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Armour, G.C., Buffa, E.S.: A heuristic algorithm and simulation approach to relative location of facilities. Management Science 9, 294–309 (1963)CrossRefGoogle Scholar
  3. 3.
    Avigad, G., Moshaiov, A.: Interactive evolutionary multiobjective search and optimization of set-based concepts. Trans. Sys. Man Cyber. Part B 39(4), 1013–1027 (2009)CrossRefGoogle Scholar
  4. 4.
    Babbar-Sebens, M., Minsker, B.S.: Interactive genetic algorithm with mixed initiative interaction for multi-criteria ground water monitoring design. Ap. Soft Comp. 12(1), 182 (2012)CrossRefGoogle Scholar
  5. 5.
    Baccianella, S., Esuli, A., Sebastiani, F.: Evaluation measures for ordinal regression. In: Proc. of the Ninth Int. Conf. on Intelligent Systems Design and App. (ISDA), Pisa, Italy (2009)Google Scholar
  6. 6.
    Brintup, A.M., Ramsden, J., Tiwari, A.: An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. Computers in Ind. 58, 279 (2007)CrossRefGoogle Scholar
  7. 7.
    Brintup, A.M., Takagi, H., Tiwari, A., Ramsden, J.: Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems. Journal of Biological Physics and Chemistry 6, 137–146 (2006)CrossRefGoogle Scholar
  8. 8.
    Cardoso, J.S., Sousa, R.: Measuring the performance of ordinal classification. International Journal of Pattern Recognition and Artificial Intelligence 25(8), 1173–1195 (2011)CrossRefGoogle Scholar
  9. 9.
    Chang, C., Lin, C.: Libsvm: a library for support vector machines (2001),
  10. 10.
    Chaudhuri, S., Deb, K.: An interactive evolutionary multi-objective optimization and decision making procedure. Applied Soft Computing 10(2), 496–511 (2010)CrossRefGoogle Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support vector networks. Maching Learning 20, 273–297 (1995)zbMATHGoogle Scholar
  12. 12.
    Frank, E., Hall, M.: A simple approach to ordinal classification. In: Proceedings of the 12th European Conference on Machine Learning, pp. 145–156 (2001)Google Scholar
  13. 13.
    García-Hernández, L., Salas-Morera, L., Arauzo-Azofra, A.: An interactive genetic algorithm for the unequal area facility layout problem. In: SOCO, pp. 253–262 (2011)Google Scholar
  14. 14.
    Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)Google Scholar
  15. 15.
    Jeong, I., Kim, K.: An interactive desirability function method to multiresponse optimization. European Journal of Operational Research 195(2), 412–426 (2009)zbMATHCrossRefGoogle Scholar
  16. 16.
    Kouvelis, P., Kurawarwala, A.A., Gutierrez, G.J.: Algorithms for robust single and multiple period layout planning for manufacturing systems. European Journal of Operational Research 63(2), 287–303 (1992)zbMATHCrossRefGoogle Scholar
  17. 17.
    Kusiak, A., Heragu, S.S.: The facility layout problem. European Journal of Operational Research 29(3), 229–251 (1987)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Li, L., Lin, H.T.: Ordinal Regression by Extended Binary Classification. In: Advances in Neural Information Processing Systems, vol. 19 (2007)Google Scholar
  19. 19.
    Liu, F., Geng, H., Zhang, Y.Q.: Interactive fuzzy interval reasoning for smart web shopping. Applied Soft Computing 5(4), 433–439 (2005)CrossRefGoogle Scholar
  20. 20.
    Luque, M., Miettinen, K., Eskelinen, P., Ruiz, F.: Incorporating preference information in interactive reference point methods for multiobjective optimation. Omega 37(2), 450 (2009)CrossRefGoogle Scholar
  21. 21.
    McCullagh, P.: Regression models for ordinal data. Journal of the Royal Statistical Society 42(2), 109–142 (1980)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Pérez-Ortiz, M., Gutiérrez, P.A., García-Alonso, C., Carulla, L.S., Pérez, J.S., Hervás-Martínez, C.: Ordinal classification of depression spatial hot-spots of prevalence. In: Proc. of the 11th Int. Conf. on Intelligent Systems Design and App (ISDA), p. 1170 (2011)Google Scholar
  23. 23.
    Sato, T., Hagiwara, M.: Idset: Interactive design system using evolutionary techniques. Computer-Aided Design 33(5), 367–377 (2001)CrossRefGoogle Scholar
  24. 24.
    Sun, B.Y., Li, J., Wu, D.D., Zhang, X.M., Li, W.B.: Kernel discriminant learning for ordinal regression. IEEE Transactions on Knowledge and Data Engineering 22, 906–910 (2010)CrossRefGoogle Scholar
  25. 25.
    Tompkins, J., White, J., Bozer, Y., Tanchoco, J.: Facilities Planning, 4th edn. Wiley, New York (2010)Google Scholar
  26. 26.
    Tong, X.: SECOT: A Sequential Construction Technique For Facility Design. Doctoral Dissertation, University of Pittsburg (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • M. Pérez-Ortiz
    • 1
    Email author
  • L. García-Hernández
    • 1
  • L. Salas-Morera
    • 1
  • A. Arauzo-Azofra
    • 1
  • C. Hervás-Martínez
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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