The performance of the AGAIS (II) algorithm for the resolution of the Flexible Job-shop scheduling problem

In our previous works, we have proposed a new approach based on genetic algorithms and the learning by injection of sequences for solving the Flexible Job-shop Scheduling Problem (FJSP). This approach was based on a joint resolution of the inherent assignment subproblem and the sequencing subproblem with total flexibility. In this paper, we develop a new strategy of learning (partial injection of sequences) in order to improve the total duration of the schedule or the makespan. We call this method AGAIS (II). Computational results show the efficiency of the proposed approach.