Most publications in shop scheduling area focus on the static scheduling problems and seldom take into account the dynamic disturbances such as machine breakdown or new job arrivals. Motivated by the computational complexity of the scheduling problems, genetic algorithms (GAs) have been applied to improve both the efficiency and the effectiveness for NP-hard optimization 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 study presents a GA-based approach combined with a feasible energy function for multiprocessor scheduling problems with resource and timing constraints in dynamic real-time scheduling. Moreover, an easy-understood genotype is designed to generate legal schedules. The results of the experiments demonstrate that the proposed approach performs rapid convergence to address its applicability and generate good-quality schedules.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence