Job-shop scheduling using genetic algorithm
Job-shop scheduling is an important step in planning and manufacturing control in the CIMS environment. Research on job-shop scheduling focuses on knowledge-based approaches and heuristic searches which are useful except for the difficulty of obtaining the knowledge. Genetic algorithms are an optimization method which use the ideas of evolution of nature. Genetic algorithms are also efficient. A novel genetic algorithm model is presented to design a job-shop schedule algorithm. Since a valid solution of scheduling is hard to search for, 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.