Comparing Schedule Generation Schemes in Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times

  • Miguel A. González
  • Camino R. Vela
  • María Sierra
  • Inés González
  • Ramiro Varela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


The Job Shop Scheduling Problem with Sequence Dependent Setup Times (SDJSS) is an extension of the Job Shop Scheduling Problem (JSS) that has interested to researchers during the last years. In this paper we confront the SDJSS problem by means of a memetic algorithm. We study two schedule generation schemas that are extensions of the well known G&T algorithm for the JSS. We report results from an experimental study showing that the proposed approaches produce similar results and that both of them are more efficient than other genetic algorithm proposed in the literature.


Genetic Algorithm Local Search Setup Time Critical Path Memetic Algorithm 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Artigues, C., Lopez, P., P.D., A.: Schedule generation schemes for the job shop problem with sequence-dependent setup times: Dominance properties and computational analysis. Annals of Operational Research 138, 21–52 (2005)Google Scholar
  2. 2.
    Bierwirth, C.: A Generalized Permutation Approach to Jobshop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)zbMATHGoogle Scholar
  3. 3.
    Brucker, P., Jurisch, B., Sievers, B.: A branch and bound algorithm for the job-shop scheduling problem. Discrete Applied Mathematics 49, 107–127 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Brucker, P., Thiele, O.: A branch and bound method for the general-job shop problem with sequence-dependent setup times. Operations Research Spektrum 18, 145–161 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Brucker, P.: Scheduling Algorithm, 4th edn. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Carlier, J., Pinson, E.: Adjustment of heads and tails for the job-shop problem. European Journal of Operational Research 78, 146–161 (1994)zbMATHCrossRefGoogle Scholar
  7. 7.
    Cheung, W., Zhou, H.: Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times. Annals of Operational Research 107, 65–81 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Dell Amico, M., Trubian, M.: Applying Tabu Search to the Job-shop Scheduling Problem. Annals of Operational Research 41, 231–252 (1993)zbMATHCrossRefGoogle Scholar
  9. 9.
    Giffler, B., Thomson, G.L.: Algorithms for Solving Production Scheduling Problems. Operations Reseach 8, 487–503 (1960)zbMATHCrossRefGoogle Scholar
  10. 10.
    González, M.A., Sierra, M.R., Vela, C.R., Varela, R.: Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules. LNCS, Springer, Heidelberg (to appear, 2006)Google Scholar
  11. 11.
    González, M.A., Vela, C.R., Puente, J., Sierra, M.R., Varela, R.: Memetic Algorithms for the Job Shop Scheduling Problem with Sequence Dependent Setup Times. In: Proceedings of ECAI Workshop on Evolutionary Computation (to appear, 2006)Google Scholar
  12. 12.
    Mattfeld, D.C.: Evolutionary Search and the Job Shop. Investigations on Genetic Algorithms for Production Scheduling, November 1995. Springer, Heidelberg (1995)Google Scholar
  13. 13.
    Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Management Science 42, 797–813 (1996)zbMATHCrossRefGoogle Scholar
  14. 14.
    Ovacik, I.M., Uzsoy, R.: Exploiting shop floors status information to schedule complex jobs. Operations Research Letters 14, 251–256 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Taillard, E.D.: Parallel Taboo Search Techniques for the Job Shop Scheduling Problem. ORSA Journal of Computing 6, 108–117 (1993)Google Scholar
  16. 16.
    Varela, R., Vela, C.R., Puente, J., Gmez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research 145, 57–71 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Varela, R., Serrano, D., Sierra, M.: New Codification Schemas for Scheduling with Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 11–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Zoghby, J., Barnes, J.W., Hasenbein, J.J.: Modeling the re-entrant job shop scheduling problem with setup for metaheuristic searches. European Journal of Operational Research 167, 336–348 (2005)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel A. González
    • 1
  • Camino R. Vela
    • 1
  • María Sierra
    • 1
  • Inés González
    • 1
  • Ramiro Varela
    • 1
  1. 1.Artificial Intelligence Center. Dep. of Computer ScienceUniversity of OviedoGijónSpain

Personalised recommendations