Research On Job Shop Scheduling Problem With Uncertainty Based On Genetic Algorithm

With the development of economy and diversification of consumer demand, the market competition is becoming increasingly fierce, the production mode of large quantities and few variety in the past is gradually replaced by high mix mode. In order to compete in the market in the long term, the enterprise must continue to optimize the production management. Production scheduling which consist of research about the relationship between resources, time, task and performance, as the core of the production management, is gradually drawn researchers' attention.In the long term, scholars use all kinds of intelligent computing methods to solve the problem. Among various intelligent algorithms, genetic algorithm is widely used because it has a weak dependence and strong robustness. At present, most of the research is dealing with certainty problems, but in the actual production process, due to the impact of various factors, the product's processing time and delivery time is often uncertain. In order to guide the actual production effectively, the job shop scheduling problem based on genetic algorithm under the condition of uncertainty is studied in this paper.The problem of job shop scheduling is studied in this paper. Our goal is to reduce the total cost of production process. Combined with the related knowledge of fuzzy number, the model of job shop scheduling problem is established in which triangular fuzzy number is used to represent the processing time, and an improved genetic algorithm is designed to solve this problem. After the comparison of the numerical examples, the effectiveness of the algorithm is verified.In order to solve the problem of scheduling problem in flexible job shop scheduling problem which is more complex, a scheduling model is established, considering the fuzzy processing time and the due date window. A genetic algorithm is designed to solve the problem, and the validity of the proposed algorithm is verified by a numerical example.Finally, this paper introduces the production status of a machine factory. According to the production data of the enterprise, the algorithm of this paper is used to solve the problem. The result shows that this method can provide good production decision for the enterprise.