Planning And Scheduling Optimization Of Job-shop In Intelligent Manufacturing System

The scheduling optimization of the job shops is a core of advanced manufacturing and modern managing technology. The problem of job shop scheduling has become one of key bottle-neck in production process. The subject is studied in this thesis and several innovations are presented.According to the information processing mechanism of immune system in biotic science, a new approach of immune genetic algorithm for job shop scheduling is proposed through combining immune algorithm with improved genetic algorithm. Aiming at the problem of job shop scheduling, the approach of distilling and injecting vaccination is solved, which is difficulty in immune algorithm. It is testified that convergence efficiency and accuracy of the immune genetic algorithm in solving ten standard job shop scheduling problems. The results indicate the proposed algorithm is competitive, being able to produce better solutions then other approach.Based on the foundation of studying job shop scheduling with dual-resource and multiple process plans, from the circs of mass incertitude complications existing in practical job shop scheduling system, the model of fuzzy job shop scheduling is formulated. The minimum fuzzy completion time or maximum average agreement index is taken as the target. An improved genetic algorithm is presented to attain the best strategy, in which coding; fitness counting, fuzzy algorithm operation is included.An integrated model is proposed, which is an integrated model of distributed and dynamic process planning & job shop scheduling. The integrated model's hierarchy structure is constructed and researched in deeply. Process planning is combined with dynamic rolling windows scheduling based on period and event-driven. The integrated system can adapt to continuous processing in a changing environment and finish the disposal in time, and reducing redesign of process planning in large scale due to outburst events. The particle swarm algorithm is introduced to job shop scheduling operation. Consequently, the function of job shop scheduling and control of the integration module is realized. Feasibility and validity of the integrated system is validated by examples.The problem of multi-objective flexible job shop scheduling optimization of batch production is studied, where multi-objects of makespan, earliness/tardiness, production cost and equipment utilization rate: total and maximum machine tool loads are concerned. The strategy of job shop scheduling optimization of batch production is proposed. The model of multi-objective scheduling optimization is set up. Aiming at improving searching efficiency and searching quality, multiple population hybrid algorithm combining both advantages of particle swarm optimization and genetic algorithm is presented. A simulation experiment is carried out to illustrate that the proposed model and algorithm is more efficiency and feasible than that used in home and abroad in existence at present.The study is supported by key project of National Natural Science Foundation, and is authenticated by the specialists of Commission of Science Technology and Industry for National Defenses. The specialists have declared:"achievements of the study are creative and in the lead of the world. The investigation shows static and dynamic scheduling problems on multi-resources constraints are firstly solved in the world".