A new leader guided optimization for the flexible job shop problem

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.

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Correspondence to Mariem Gzara.

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Naifar, F., Gzara, M. & Loukil, M.T. A new leader guided optimization for the flexible job shop problem. J Comb Optim 39, 602–617 (2020). https://doi.org/10.1007/s10878-018-00373-y

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Keywords

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