Research On Optimization Methods For Uncertainty Job Shop Scheduling

In recent years,the uncertainty of job shop scheduling problem caused much attention.The introduction of uncertainty has greatly increased the degree of production scheduling problem analysis and modeling of difficulty.At the same time,it is closer to actual production,and it can guide the actual production optimization.Therefore,the study of uncertain job shop scheduling problem is very necessary,both in theory and in practice a wide range of practical and important.In the study of uncertainty job shop scheduling problem,Problem modeling and optimization methods for problems are two important components of the work.there are many ways to modeling the problem of uncertainty job shop scheduling. In recent years, fuzzy theory is applied to the field of shop scheduling. Because job shop scheduling probles have some fuzzy features, Fuzzy theory is well suited for modeling of the Job shop Scheduling Problem. Currently, the application of fuzzy numbers to deal with the problem of uncertain parameters has received wide attention of the scheduling area.Among the methods of the job shop scheduling optimization, Genetic algorithms is attentioned and applied by researchers for its versatility and simple. However, the genetic algorithm always appear premature convergence in the Application process.For this problem, this paper using fuzzy theory to model the uncertainty job shop scheduling problem, And designed an improved genetic algorithm. The algorithm introduces the conflict resolution mechanisms in the decoding.and it can rapidly improve the quality of the chromosomes in each generation, accelerate the speed of evolution of chromosome. Niche technology with the exclusion mechanism introduced late in the evolution. Maintain the diversity of the population effectively. Avoid premature convergence of the algorithm, Enhance the overall performance of the algorithm. Finally, the example shows the effectiveness of the algorithm.