The Research On Job-Shop Scheduling Problem Based On The Immune Genetic Algorithm

The optimal production scheduling being is the core in production and operational management to maximize business performance with high efficiency and low production costs. Consequently, the research and the development of advanced planning and scheduling has been an attractive subject in academic and industrial communities.GA is one of the most widely evolutionary computation methods in scheduling optimization has irreplaceable advantages. According to the information processing mechanism of an immune system in biotic science, on the basis of simple genetic algorithm, we propose a new immune genetic algorithm for job-shop scheduling through combining immune algorithm with improved genetic algorithm. New algorithm based on selection probability of vector distance is proposed and the general expressing form of the kind of selection probability is given. Meanwhile the immune vaccine is introduced into immune genetic algorithm on selection probability of vector distance to prevent the algorithm degenerative during the process of optimization. Finally we verify the convergence efficiency and accuracy of the immune genetic algorithm in solving standard job-shop scheduling problems. The verification results indicate that it is better than simple genetic algorithm.