Natural Computing

, Volume 13, Issue 2, pp 179–192 | Cite as

A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

  • Raúl Mencía
  • María R. SierraEmail author
  • Carlos Mencía
  • Ramiro Varela


The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic job-shop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.


Job shop scheduling problem with operators Genetic algorithms Lamarckian evolution Schedule generation schemes 



This work has been supported by the Spanish Ministry of Science and Innovation under research project MICINN-FEDER TIN2010-20976-C02-02 and by FICYT under Grant BP09105.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Raúl Mencía
    • 1
  • María R. Sierra
    • 1
    Email author
  • Carlos Mencía
    • 1
  • Ramiro Varela
    • 1
  1. 1.Department of Computer ScienceUniversity of OviedoGijónSpain

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