Scheduling multi-processor tasks with resource and timing constraints using genetic algorithm

The job-shop scheduling problems have been categorized as NP-complete problems. The exponential growth of the time required to obtain an optimal solution makes the exhaustive search for global optimal schedules very difficult or even impossible. Recently, genetic algorithms have shown the feasibility to solve the job-shop scheduling problems. However, a pure GA-based approach tends to generate illegal schedules due to the crossover and the mutation operators, it is often the case that the gene expression or the genetic operators need to be specially designed to fit the problem domain or some other schemes may be combined to solve the scheduling problems. This paper presents a GA-based approach with a feasible energy function to generate good-quality schedules. In our work, we design an easy-understood genotype to generate legal schedules and the proposed approach converges rapidly.