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A new leader guided optimization for the flexible job shop problem

  • Fraj Naifar
  • Mariem GzaraEmail author
  • Moalla Taicir Loukil
Article
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Abstract

The FJSP is an extension of the classical job shop problem which has been proven to be among the hardest combinatorial optimization problems, by allowing an operation to be operated on more than one machine from a machine set, with possibility of variable performances. In this work, we have designed a co-evolutionary algorithm that applies adaptively multiple crossover and mutation operators. In the evolution process, all new generated individuals are improved by local search. Combined with a new leader tree guided optimization search, the hybrid algorithm has discovered 2 new optimal solutions for instances of Hurink et al. (Oper Res Spektrum 15(4):205–215, 1994). In general, the outcomes of simulation results and comparisons demonstrate comparable results. The leader guided optimization has shown its effectiveness for minimizing the makespan in a FJSP, but it is not limited to this environment.

Keywords

Scheduling Flexible job shop problem Evolutionary algorithm Leader guided optimization Multiple crossovers 

Notes

References

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Digital Research Center of SfaxSfaxTunisia
  2. 2.Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACLSfaxTunisia
  3. 3.University of Monastir and Computer ScienceMonastirTunisia
  4. 4.High School of Mathematics and Computer ScienceMonastirTunisia
  5. 5.Faculty of Business AdministrationTabuk UniversityTabukKingdom of Saudi Arabia

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