The Research Of The Static Scheduling Algorithm Based On Manufacturing Execution System

In the modern era, with the development of automatic control and computer technology. MES, because of its larger ability of utilizing the enterprise’s resources and the superriority of greatly improving the production efficiency, makes it get more and more accepted. For such a complex、dynamic and discrete system, the scheduling problem of MES is very important. The excellent scheduling plans make the Job Shop Scheduling Problem get much attention by people because of its very important role in increasing production efficiency and reducing the cost of product, this is also the significance of writing the paper. The paper firstly does some research on the genetic algorithm, and programs the genetic algorithm based on vc++platform. Then ues the designed genetic algorithm to solve two Benchmark problem about FT06(6x6) and FT10(10×10), getting ideal results. At last, according to the a project of159factory, this paper analyzes and studies the function structure of manufacturing execution systems (MES), and begin to work for the Job Shop Scheduling Problem of flexible manufacturing workshop, and ues the designed hybrid genetic algorithm to solve the problem, and eventually obtain the Gantt chart that can be used to guide the production scheduling. Because the paper researches on the static workshop scheduling, so many factors are not in the considered range, such as abrupt、indeterminate and random etc.This paper joins the shortest time of machine processing into the algorithm. There is the case that a process can be done anyone of the machines in the flexible manufacturing workshop. It can find the local optimum, greatly reduced the search domain of genetic algorithm, and improve the efficiency of the algorithm as well as the quality of the individual by using the shortest time of machine processing method to select the processing machine. Combined the standard genetic algorithm with the shortest time of machine processing, the efficiency of the hybrid genetic algorithm is much lower than the standard genetic algorithm. It is that the hybrid genetic algorithm fully proves the ability of exploring the search domain and the superiority of the hybrid genetic algorithm combining the advantages of genetic algorithm and the heuristic method.