Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules

  • Miguel A. González
  • María Sierra
  • Camino R. Vela
  • Ramiro Varela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


The Job Shop Scheduling is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last years. In this work we propose a Genetic Algorithm hybridized with a local search method that searches over the space of semi-active schedules and a heuristic seeding method that generates active schedules stochastically. We report results from an experimental study over a small set of selected problem instances of common use, and also over a set of big problem instances that clarify the influence of each method in the Genetic Algorithm performance.


Genetic Algorithm Search Space Local Search Problem Instance Critical Path 
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.
    Bierwirth, C.: A Generalized Permutation Approach to Jobshop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)zbMATHGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Dell Amico, M., Trubian, M.: Applying Tabu Search to the Job-shop Scheduling Problem. Annals of Operational Research 41, 231–252 (1993)zbMATHCrossRefGoogle Scholar
  5. 5.
    Giffler, B., Thomson, G.L.: Algorithms for Solving Production Scheduling Problems. Operations Reseach 8, 487–503 (1960)zbMATHCrossRefGoogle Scholar
  6. 6.
    Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research 113, 390–434 (1999)zbMATHCrossRefGoogle Scholar
  7. 7.
    Mattfeld, D.C.: Evolutionary Search and the Job Shop. In: Investigations on Genetic Algorithms for Production Scheduling, Springer, Heidelberg (1995)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Yamada, T., Nakano, R.: Scheduling by Genetic Local Search with multi-step crossover. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 960–969. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel A. González
    • 1
  • María Sierra
    • 1
  • Camino R. Vela
    • 1
  • Ramiro Varela
    • 1
  1. 1.Department of Computing Artificial Intelligence CenterUniversity of OviedoGijónSpain

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