Job-shop scheduling, a typical NP-complete problem, is an important step in planning and manufacturing control of CIMS environment. Researches on job-shop scheduling focus on knowledge-based approach and heuristic searching which are useful except the difficulty of getting knowledge. Genetic algorithms are optimization methods which use the ideas of the evolution of the nature. Simple as genetic algorithms are, they are efficient  . Three novel genetic algorithms model, such as decimal idle time coding genetic algorithm(DITCGA), binary idle time coding genetic algorithm(BITCGA), and adaptive idle time coding genetic algorithm(AITCGA), are presented to design job-shop scheduling algorithm in this paper. Using the idle processing time to code this problem, we efficiently reduce the solution space. In our approaches, adaptive learning mechanism is applied to guide the searching or evolution process. The simulation results show the efficiency of these approaches.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|State||Published - Dec 1 1996|
ASJC Scopus subject areas
- Hardware and Architecture
- Control and Systems Engineering