Evaluating Schedule Performance in Flexible Job-Shops

  • Imed Kacem
  • Pierre Borne


In this paper, we are interested in the multi-objective evaluation of the schedule performance in the flexible job shops. The Flexible Job Shop Scheduling Problem (FJSP) is known in the literature as one of the hardest combinatorial optimization problems and presents many objectives to be optimized. In this way, we aim to determine a set of lower bounds for certain criteria which will be able to characterize the feasible solutions of such a problem. The studied criteria are the following: the makespan, the workload of the critical machine, and the total workload of all the machines. Our study relates to the determination of a practical method using fuzzy logic in order to evaluate the representative performance of the production system. The performance of such fuzzy evaluation is shown by applying an evolutionary algorithm and by comparing the values of the different solutions with the corresponding lower-bounds.


Schedule Problem Completion Time Tabu Search Crossover Operator Precedence Constraint 
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.
    P. Brandimarte, Routing and scheduling in Flexible job shops by tabu search, Annals of Operations Research 41 (1993) 157–183.MATHCrossRefGoogle Scholar
  2. 2.
    I. Kacem, S. Hammadi, P. Borne, Approach by Localization and Multi-objective Evolutionary Optimization for Flexible Job-Shop Scheduling Problems. IEEE Transactions on Systems, Man, and Cybernetics, PART C, Vol 32, N°1, ppl-13, 2002.Google Scholar
  3. 3.
    P. Brucker, J. Neyer, Tabu search for the multi-mode job-shop problem, OR-Spektrum 20, 1998, p 21–28.MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    K. Mesghouni, Application des algorithmes évolutionnistes dans les problèmes d’optimisation en ordonnancement de production, Thèse, USTL, 5 janvier, 1999, France.Google Scholar
  5. 5.
    I. Kacem, Ordonnancement multicritère des job-shops flexibles: formulation, bornes inférieures et approche évolutionniste coopérative. Ph.D Thesis. Ecole Centrale de Lille. January 6th, 2003, France.Google Scholar
  6. 6.
    S. Dauzère-Pérès, J. Paulli, An integrated approach for modelling and solving the general multiprocessor job-shop scheduling problem using tabu search, Annals of Oper. Res., 70 (1997), pp. 281–306.MATHCrossRefGoogle Scholar
  7. 7.
    Mastrolilli M., Gambardella L.M., (1998). Effective Neighborhood Functions for the Flexible Job Shop Problem, Journal of Scheduling, Volume 3, Issue 1, 2000. Pages: 3–20.Google Scholar
  8. 8.
    S. Dauzère-Pérès, W. Roux, J.B. Lasserre, Multi-Resource Shop Scheduling Problem with Resource Flexibility, EJOR, n°107, pp. 289–305, 1998.Google Scholar
  9. 9.
    J. Carlier, The one-machine sequencing problem, European Journal of Operational Research, 11 (1) (1982), pp 42–47.MathSciNetCrossRefGoogle Scholar
  10. 10.
    B. Jurisch, Scheduling Jobs in Shops with Multi-purpose Machines, Ph.D thesis, Fachbereich Mathematik/Informatik, Universitat Osnabruck, 1992.Google Scholar
  11. 11.
    J. Carlier, Scheduling jobs with release dates and tails on identical machines to minimize the makespan, European Journal of Operational Research, 29 (1987) 298–306, North-Holland.Google Scholar
  12. 12.
    I. Kacem, S. Hammadi, P. Borne, Bornes Inférieures pour les Problèmes d’Ordonnancement des Job-shop Flexibles, CIFA’02, Juillet, 2002, Nantes, France.Google Scholar
  13. 13.
    J.C. Billaut, J. Carlier, E. Néron, Ordonnancement d’ateliers à ressources multiples. Ordonnancement de la production, Edition Hermès, 2002, France.Google Scholar
  14. 14.
    J. Carlier, An algorithm for solving the job shop problem. Management science, Vol. 35, 1989, p164–176.MathSciNetCrossRefGoogle Scholar
  15. 15.
    I. Kacem, S. Hammadi, P. Borne, Approche évolutionniste modulaire contrôlée pour le problème du type job-shop flexible, Proceedings of JDA’2001, Journées Doctorales d’Automatique, 25–27 septembre 2001, Toulouse, France (in french) (available at the web address: Scholar
  16. 16.
    I. Kacem, S. Hammadi, P. Borne, Pareto-optimality Approach for Flexible Job-shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic. Journal of Mathematics and Computers in Simulation, Elsevier, 2002.Google Scholar
  17. 17.
    D. Boucon, Ordonnancement d’Atélier: aide au choix de règles de priorité, Thèse ENSAE, Toulouse, 1991, France.Google Scholar
  18. 18.
    G. Bel, J-B. Cavaillé, Approche simulatoire, Chapitre 6 de: Ordonnancement de la production, Sous la direction de P. Lopez et de F. Roubellat, Hermès, 2001. France.Google Scholar
  19. 19.
    M-C. Portmann, Study on Crossover Operators Keeping good schemata for some scheduling problems, Genetic and Evolutionary Computation Conference, Las Vegas, 8–12 juillet 2000, USA.Google Scholar
  20. 20.
    L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New-York, 1990, USA.Google Scholar
  21. 21.
    Y. W. Leung, Y. Wang, Multiobjective Programming Using Uniform Design and Genetic Algorithm, IEEE/SMC Transactions, Part C, Vol 30, August 2000, pp 293–303.Google Scholar
  22. 22.
    I. Hurink. B. Jurisch. M. Thole, Tabu search for the job-shop scheduling problem with multi-purpose machines, OR-Spektrum 15, pp 205–215.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Imed Kacem
    • 1
  • Pierre Borne
    • 1
  1. 1.LAIL. Ecole Centrale de LilleUK

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