Section: New Results

Surrogate-assisted multiobjective evolutionary algorithm for fuzzy job shop problems

Participants: E-G. Talbi and Juan José Palacios, Jorge Puente, Camino R. Vela, Inés Gonzalez-Rodrıguez (Univ. Oviedo, Spain)

We have considered 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.