Fuzzy Evolutionary Approach for Multiobjective Combinatorial Optimization: Application to Scheduling Problems

  • Imed Kacem
  • Slim Hammadi
  • Pierre Borne
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 126)

Abstract

Most combinatorial problems are complex and very hard to solve. That is why, lots of methods focus on the optimization according to a single criterion. The combining of several criteria presents additional complexity and new problems.

Keywords

Membership Function Multiobjective Optimization Pareto Frontier Pareto Solution Nondominated Solution 
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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References

  1. 1.
    Zitzler, E., Deb, K., Thiele, L., Coello, C., Corne, D., 2001. Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, Vol. 1993, Springer Verlag.Google Scholar
  2. 2.
    Sarker, R., Abbas, H.A., Newton, C., 2001. Solving multiobjective optimization problems using evolutionary algorithm. Proceedings of International CIMCA Conference, July 9–11, 2001, Las Vegas, Nevada, USA.Google Scholar
  3. 3.
    Ishibuchi, H., Murata, T., 1998. A Multiobjective Genetic Local Search Algorithm and Its Application to Flowshop Scheduling. IEEE/SMC Transactions, Part C, Vol 28, August 1998, pp 392–403.Google Scholar
  4. 4.
    Fonseca, C.M., Fleming, P.J., 1998, (a). Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms: Part I: A Unified Formulation. IEEE/SMC Transactions, Part A, Vol 28, January 1998, pp 2637.Google Scholar
  5. 5.
    Fonseca, C.M., Fleming, P.J., 1998, (b). Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms: Part II: Application Example. IEEE/SMC Transactions, Part A, Vol 28, January 1998, pp 38–47.Google Scholar
  6. 6.
    Kacem, I., Hammadi, S., Borne, P., 2002, (a). Approach by Localization and Multiobjective Evolutionary Optimization for Flexible Job-Shop Scheduling Problems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Reviews and Applications, Vol 32, N°1, 2002.Google Scholar
  7. 7.
    Bachelet, V., Hafidi, Z., Preux, P., Talbi, E-G., 1998. Vers la coopération des métaheuristiques. Calculateurs parallèles, Vol. 9, N. 2.Google Scholar
  8. 8.
    Davis, L., 1990. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New-York, USA.Google Scholar
  9. 9.
    Portmann, M-C., 2000. Study on Crossover Operators Keeping good schemata for some scheduling problems. Genetic and Evolutionary Computation Conference, July 8–12, 2000, Las Vegas, USA.Google Scholar
  10. 10.
    Ghedjati, F., 1994. Résolution par des heuristiques dynamiques et des algorithmes génétiques du problème d’ordonnancement de type job-shop généralisé. Ph.D Thesis, University of Paris VI, December 15, 1994, FRANCE.Google Scholar
  11. 11.
    Michalewicz, Z., 1992. Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag.Google Scholar
  12. 12.
    Fogel, D.B., 1994. An Introduction to Simulated Evolutionary Optimization. IEEE Transactions on Neural Networks, Vol. 5, N°1, Jan 1994, pp 3–14.Google Scholar
  13. 13.
    Schaffer, J.D., 1985. Multiobjective Optimization with Vector Evaluated Genetic Algorithms. in Proc. 1st Int. Conf. Genetic Algorithms, 1985, pp 93–100.Google Scholar
  14. 14.
    Leung, Y. W., Wang, Y., 2000. Multiobjective Programming Using Uniform Design and Genetic Algorithm. IEEE/SMC Transactions, Part C, Vol 30, August, 2000, pp 293–303.Google Scholar
  15. 15.
    Gzara, M., 2001. Méthode coopérative d’aide multicritère à l’ordonnancement flou. Ph.D. Thesis, Ecole Centrale de Lille et Université de Lille 1. FRANCE.Google Scholar
  16. 16.
    Kacem, I., Hammadi, S., Borne, P., 2002 (b). Pareto-optimality Approach for Flexible Job-shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic. Journal of Mathematics and Computers in Simulation, Elsevier.Google Scholar
  17. 17.
    Carlier, J., 1989. An algorithm for solving the job shop problem. Management science, Vol. 35, pp 164–176.MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Hillier, F.S., Lieberman, G.J., 1967. Introduction to Operations Research. Holden-Day, San Fransisco, CA.Google Scholar
  19. 19.
    Dauzère-Pérès,S., Paulli,J. 1997. An integrated approach for modelling and solving the general multiprocessor job-shop scheduling problem using tabu search. Annals of Oper. Res., 70, pp. 281–306.Google Scholar
  20. 20.
    Kacem, I., Hammadi, S., Borne, P., 2002, (c). Bornes Inférieures pour les Problèmes d’Ordonnancement des Job-shop Flexibles. Proceedings of CIFA’02, July 2002, Nantes, FRANCE.Google Scholar
  21. 21.
    Kacem, I., Hammadi, S., Borne, P., 2001, (a). Approach by Localization and Genetic Manipulations Algorithm for Flexible Job-shop Problems. Proceedings of International IEEE Conference on Systems, Man, and Cybernetics, October 7–10, 2001, pp 2599–2604, Tucson, Arizona, USA.Google Scholar
  22. 22.
    Kacem, I., Hammadi, S., Borne, P., 2001, (b). Direct Chromosome Representation and Advanced Genetic Operators for Flexible Job-shop Problems. Proceedings of International CIMCA Conference, pp 123–131, July 9–11, Las Vegas, Nevada, USA.Google Scholar
  23. 23.
    Mesghouni, K., 1999. Application des algorithmes évolutionnistes dans les problèmes d’optimisation en ordonnancement de production. Ph.D Thesis, University of Lillel, January 5, 1999, FRANCE.Google Scholar
  24. 24.
    Kacem, I., Hammadi, S., Borne, P., 2001, (c). Multiobjective Optimization for Flexible Job-Shop Scheduling Problem: Hybridization of Genetic Algorithms with Fuzzy Logic. Proceedings of IFDICON’2001, European Workshop on Intelligent Forecasting, Diagnosis and Control, 24–28 June, 2001, Santorini, GREECE.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Imed Kacem
    • 1
  • Slim Hammadi
    • 1
  • Pierre Borne
    • 1
  1. 1.Laboratoire d’Automatique et Informatique de LilleEcole Centrale de LilleVilleneuve d’AscqFrance

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