earticle

영어

MES job shop scheduling problem is the core module, and its essence is a kind of resource constraints, time constraints and process constraints such as combinatorial optimization problems, research and application of job shop scheduling problem for China's manufacturing sector improve management, productivity, and the implementation of advanced manufacturing strategies are important, therefore, job shop scheduling research has theoretical and practical significance. In this paper, the slow evolution of GA algorithm will occur, or premature, and the SA algorithm is introduced to the crossover and mutation, genetic simulated annealing algorithm is proposed - GASA algorithm, mathematical model is given algorithm, the algorithm model, the algorithm process. GA parallel sampling of the time optimization algorithm performance can be improved, and the control of SA binding guidelines to control the convergence of the algorithm to avoid prematurity. The classical scheduling problem FT06, GASA correctness of the algorithm is verified and compared with the traditional GA algorithm is verified GASA efficiency of the algorithm.