Study On Job-Shop Scheduling Algorithm Of MES

With the development of the manufacturing industry, Job Shop Scheduling has high standard for the details of production process. As the workshop production scheduling has been proven to be NP-hard problem, we should work hard to seek the optimal solution. And with the rapid economic development and manufacturing demand improved, the importance of the workshop production scheduling highlights today.Firstly, for the complexity of scheduling, this paper compares the traditional optimization algorithms with intelligent optimization algorithms on the work-shop scheduling problems. After comparison, using intelligent optimization algorithms to solve the problems is a best way. Through the introduction of the current intelligent optimization algorithm are applied in the workshop production scheduling. Combined with the actual production situation of enterprises, it was selected genetic algorithm as the main algorithm. In this paper, genetic algorithm has premature convergence and drawback of slow convergence. And the improvement of this phenomenon is presented by means of simulated annealing algorithm. At the high temperature, the rate of mutation is slowed down, and the mutation rate is accelerated, and the method is applied to the job shop scheduling problem. The results show that the hybrid algorithm overcomes the shortcomings of the simple genetic algorithm and the simulated annealing algorithm in the job shop scheduling optimization.Finally, in a mould company processing plant in Guangdong Province as the object, in-depth analysis of the production of the workshop, and in the study of MES function frame based, for the workshop production management actual situation, It was built a information management system for production workshop. It was use C sharp to write in Microsoft Visual 2010 and the SQL Server 2008. It can offer the production data management, production scheduling, data analysis and so on. In the actual operation, it can reducing the labor of company costs and improve productivity.