Finite Capacity Job-Shop Scheduling Based On Hybrid Genetic Algorithm

Wiht the word entering informationization time, the environment which the manufacturing industry locates unceasingly changes, Majority enterprise's production pattern transformed by the past mass production as the single-piece and small-batch production.Nnder this kind of production environment, how the enterprise utilize the limited resources to fast response customer demand, reduce the production cost, guarante delivers on time, becomes the manufacture enterprise an important condition which survives in the competition. The job-shop scheduling is the core of manufacture productionsystem,it can improve the production efficiencyand product quality, reduce the cost, and so on .Therefore, the workshop scheduling is one of hot topics which in the manufacturing production studies. Because the workshop scheduling has the dynamic randomness, the computation complex characteristic, at present not yet appears the effective theory solves the workshop scheduling problem. Seeks the effective dispatch method and the optimization techniques, has the important theory significance and the practical value regarding the enhancement manufacture enterprise's modernized level.On the basis of the technical review on the domestic and foreign research,this thesis has an extensive and systematic study on the job-shop scheduling.Firstly,direct against the limitation of classical job-shop scheduling, combine the actual conditions of the workshop, deployment model of finite capacity job-shop scheduling. Secondly, In order to avoid permature convergence and slow convergent speed at later evolution process of traditional genetic algorithm,a hybrid algorithm was proposed to solve finite capacity job-shop scheduling problems. This algorith is effectively combined with the merits of simulated annealing and multigroup genetic, and simulation results validate the effectiveness of the proposed algorithm. Finally, develop the finite capacity job-shop scheduling system.