Optimization Method Of Flexible Job-Shop Scheduling With Lot-splitting Production In Disturbances Uncertain

In response to the increasingly competitive environment, it is a requirement for modern manufacturing system to create a maximize target value under the limited production resource. Job shop scheduling is a key factor to influence the enterprise production cost, production efficiency and quality of service, and it is also an important step from multitudinous pushing production to slight pulling production. Scholars at home and abroad pay extensive attention to the model of job shop scheduling because of its high complexity and solving difficulty. Therefore the study of this problem has important academic value and practical significance. This paper researches the method of the dynamic batch scheduling using the particle swarm optimization aimed at job shop scheduling problem with uncertain disturbance factors, special contents as follows:First of all, paper introduces the background and significance of the study. Consist of the research results of the workshop scheduling problem; the primary method and procedure of the study are also included.The second part is the theoretical basis of the paper which explains the definition and specialty of job shop scheduling problem and the origin, basic idea, process, improved mode of the particle swarm optimization and so on.Thirdly, paper analysis the current particle swarm optimization and its improved solving way, a particle swarm optimization whose population diversity can be controlled is designed, and paper deals with the static job shop batch scheduling problem based on the algorithm.Once more, a dynamic optimization method of the batch scheduling problem in uncertain condition is designed. Paper divides the disturbance factors into three levels according to the disturbance influences, and uses multilevel rescheduling to reduce interference for different disturbance.In the end, paper tests the static scheduling algorithm and the dynamic method using relevant instances. The main research details and achievements are summarized, and the further study direction is put forward.