Hybrid Tabu Search for Fuzzy Job Shop

  • Juan José Palacios
  • Jorge Puente
  • Inés González-Rodríguez
  • Camino R. Vela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)


We consider the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, with uncertainty in task durations that we model using fuzzy numbers. We propose a tabu search algorithm for minimising the expected makespan based on reversing arcs within critical blocks. We test the algorithm and then combine it with a genetic algorithm from the literature so we can observe the synergy effect, obtaining better results with the hybrid algorithm than with its components by separate. Finally we compare our hybrid algorithm with a memetic algorithm from the literature and show that even in similar times, our method is better in terms of expected makespan.


Local Search Fuzzy Number Tabu Search Memetic Algorithm Tabu List 
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.
    Bortolan, G., Degani, R.: A review of some methods for ranking fuzzy subsets. In: Dubois, D., Prade, H., Yager, R. (eds.) Readings in Fuzzy Sets for Intelligence Systems, pp. 149–158. Morgan Kaufmann, Amsterdam (1993)Google Scholar
  2. 2.
    Brucker, P., Knust, S.: Complex Scheduling. Springer (2006)Google Scholar
  3. 3.
    Dell’ Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Annals of Operational Research 41, 231–252 (1993)zbMATHCrossRefGoogle Scholar
  4. 4.
    Dubois, D., Fargier, H., Fortemps, P.: Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge. European Journal of Operational Research 147, 231–252 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Fortemps, P.: Jobshop scheduling with imprecise durations: a fuzzy approach. IEEE Transactions of Fuzzy Systems 7, 557–569 (1997)CrossRefGoogle Scholar
  6. 6.
    González Rodríguez, I., Vela, C.R., Hernández-Arauzo, A., Puente, J.: Improved local search for job shop scheduling with uncertain durations. In: Proceedings of ICAPS-2009, pp. 154–161. AAAI Press, Thesaloniki (2009)Google Scholar
  7. 7.
    González Rodríguez, I., Vela, C.R., Puente, J.: A memetic approach to fuzzy job shop based on expectation model. In: Proceedings of IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, pp. 692–697. IEEE, London (2007)Google Scholar
  8. 8.
    González Rodríguez, I., Vela, C.R., Puente, J., Varela, R.: A new local search for the job shop problem with uncertain durations. In: Proceedings of ICAPS-2008, pp. 124–131. AAAI Press, Sidney (2008)Google Scholar
  9. 9.
    Herroelen, W., Leus, R.: Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research 165, 289–306 (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics–Part C: Applications and Reviews 67(3), 392–403 (1998)CrossRefGoogle Scholar
  11. 11.
    Liu, B., Liu, Y.K.: Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems 10, 445–450 (2002)CrossRefGoogle Scholar
  12. 12.
    Niu, Q., Jiao, B., Gu, X.: Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time. Applied Mathematics and Computation 205, 148–158 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    José Palacios, J., González-Rodríguez, I., Vela, C.R., Puente, J.: Particle swarm optimisation for open shop problems with fuzzy durations. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 362–371. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Petrovic, S., Fayad, S., Petrovic, D., Burke, E., Kendall, G.: Fuzzy job shop scheduling with lot-sizing. Annals of Operations Research 159, 275–292 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Pinedo, M.L.: Scheduling, 3rd edn. Theory, Algorithms, and Systems. Springer (2008)Google Scholar
  16. 16.
    Puente, J., Vela, C.R., González-Rodríguez, I.: Fast local search for fuzzy job shop scheduling. In: Proceedings of ECAI 2010, pp. 739–744. IOS Press (2010)Google Scholar
  17. 17.
    Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. European Journal of Operational Research 120, 393–407 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Słowiński, R., Hapke, M. (eds.): Scheduling Under Fuzziness, Studies in Fuzziness and Soft Computing, vol. 37. Physica-Verlag (2000)Google Scholar
  19. 19.
    Tavakkoli-Moghaddam, R., Safei, N., Kah, M.: Accessing feasible space in a generalized job shop scheduling problem with the fuzzy processing times: a fuzzy-neural approach. Journal of the Operational Research Society 59, 431–442 (2008)zbMATHCrossRefGoogle Scholar
  20. 20.
    Van Laarhoven, P., Aarts, E., Lenstra, K.: Job shop scheduling by simulated annealing. Operations Research 40, 113–125 (1992)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan José Palacios
    • 1
  • Jorge Puente
    • 1
  • Inés González-Rodríguez
    • 2
  • Camino R. Vela
    • 1
  1. 1.A.I. Centre and Department of Computer ScienceUniversity of OviedoSpain
  2. 2.Department of Mathematics, Statistics and ComputingUniversity of CantabriaSpain

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