Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times
The major motivation of ACGA2 is to take the bi-variate probabilistic models into the consideration. We further provide a theoretical analysis of the two-models EDAs. This studied conducted extensive experiments on the single machine scheduling problems with sequence-dependent setup times in a common due-date environment. The experimental result shows the proposed ACGA2 outperforms ACGA significantly because the average error ratio of ACGA2 is half of ACGA.
acga scheduling problems sequence-dependent setup times common due-date bi-variate edas variable neighborhood search