Dynamic real-time scheduling for multi-processor tasks using genetic algorithm

With the exponential growth of time to obtain an optimal solution, the job-shop scheduling problems have been categorized as NP-complete problems. The time complexity makes the exhaustive search for a global optimal schedule infeasible 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 tailored 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. This work concentrates mainly on dynamic real-time scheduling problems with constraint satisfaction. In our work, we design an easy-understood genotype to generate legal schedules and induce that the proposed approach can converge rapidly to address its applicability