Surrogate-Assisted Multiobjective Evolutionary Algorithm for Fuzzy Job Shop Problems

  • Juan José Palacios
  • Jorge Puente
  • Camino R. Vela
  • Inés González-RodríguezEmail author
  • El-Ghazali Talbi
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


We consider a job shop scheduling problem with uncertain processing times modelled as triangular fuzzy numbers and propose a multiobjective surrogate-assisted evolutionary algorithm to optimise not only the schedule’s fuzzy makespan but also the robustness of schedules with respect to different perturbations in the durations. The surrogate model is defined to avoid evaluating the robustness measure for some individuals and estimate it instead based on the robustness values of neighbouring individuals, where neighbour proximity is evaluated based on the similarity of fuzzy makespan values. The experimental results show that by using fitness estimation, it is possible to reach good fitness levels much faster than if all individuals are evaluated.


Fuzzy job shop Robust scheduling Multiobjective evolutionary algorithm Surrogate fitness 



This research has been supported by the Spanish Government under Grant FEDER TIN2013-46511-C2-2-P.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Juan José Palacios
    • 1
  • Jorge Puente
    • 1
  • Camino R. Vela
    • 1
  • Inés González-Rodríguez
    • 2
    Email author
  • El-Ghazali Talbi
    • 3
  1. 1.Department of ComputingUniversity of OviedoGijónSpain
  2. 2.Department of Mathematics, Statistics and ComputingUniversity of CantabriaSantanderSpain
  3. 3.INRIA Laboratory, CRISTAL/CNRSUniversity Lille 1Villeneuve d’AscqFrance

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