high complexity of permutations or combination of jobs, machines, and resource constraints. In this research, two multi-objective genetic algorithms are proposed to deal with such a complicated real-world case. Real-world instances are applied as well to uate the proposed algorithms. The result indicates that both VMOGA and AMOGA are effective.
