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.
|Number of pages||7|
|Journal||Proceedings - International Computer Software and Applications Conference|
|Publication status||Published - 2004 Dec 1|
|Event||Proceedings of the 28th Annual International Computer Software and Applications Conference, COMPSAC 2004 - Hong Kong, China, Hong Kong|
Duration: 2004 Sept 28 → 2004 Sept 30
All Science Journal Classification (ASJC) codes
- Computer Science Applications