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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shu-Chen Cheng Yueh-Min Huang |
| Copyright Year | 2004 |
| Description | Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Southern Taiwan Univ. of Technol. (Shu-Chen Cheng) |
| Abstract | 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 |
| Starting Page | 154 |
| Ending Page | 160 |
| File Size | 552974 |
| Page Count | 7 |
| File Format | |
| ISBN | 0769522092 |
| ISSN | 07303157 |
| DOI | 10.1109/CMPSAC.2004.1342820 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-09-28 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Dynamic scheduling Genetic algorithms Optimal scheduling Job shop scheduling Processor scheduling NP-complete problem Genetic mutations Gene expression Hopfield neural networks Flexible manufacturing systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Software |
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