Job-shop scheduling problems are among most studied
problems in last years because of their importance for industries and
manufacturing processes. They are classified as NP-hard problems in
the strong sense. In order to tackle these problems several models and
methods have been used. In this paper, we propose a hybrid metaheuristic
composed of a genetic algorithm and a tabu search algorithm
to solve the stochastic job-shop scheduling problem. Our contribution is
based on a study of the perturbations that affect the processing times of
the jobs. These perturbations, due to machine failures, occur according
to a Poisson process; the results of our approach are validated on a set
of instances originating from the OR-Library [14]. On the basis of these
instances, the hybrid metaheuristic is used to solve the stochastic jobshop
scheduling problem with the objective of minimizing the makespan
as first objective and the number of critical operations as second objective
during the robustness analysis. Indeed, the results show that a high
value of the number of critical operations is linked to high variations of
the makespan of the perturbed schedules, or in other words to a weak
robustness of the relating GA’s best schedule. Consequently, critical operations
are not only good targets for optimizing a schedule, but also a
clue of its goodness when considering stochastic and robustness aspects:
the less critical operations it contains, the better it is.