Abstract
Job-shop scheduling is an important step in planning and manufacturing control of CIMS environment. Research on job-shop scheduling focus on knowledge-based approach and heuristic search which are useful except the difficulty of getting knowledge. Genetic algorithms are optimization method which use the ideas of the evolution of the nature. Simple as genetic algorithms are, they are efficient. A novel genetic algorithms model is presented to design job-shop schedule algorithm in this paper. Since the valid solution of scheduling is hard to search, we introduce a punishment factor to distinguish the valid solution and invalid solution in the solution space. The simulation result shows the efficiency of this approach.
| Original language | English (US) |
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| Title of host publication | International Conference on Signal Processing Proceedings, ICSP |
| Publisher | IEEE |
| Pages | 1441-1444 |
| Number of pages | 4 |
| Volume | 2 |
| State | Published - Dec 1 1996 |
| Event | Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) - Beijing, China Duration: Oct 14 1996 → Oct 18 1996 |
Other
| Other | Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) |
|---|---|
| City | Beijing, China |
| Period | 10/14/96 → 10/18/96 |
ASJC Scopus subject areas
- Signal Processing
