Application And Research On Knowledge Database With Improved Adaptive Genetic Algorithm

Nowdays, a variety of scheduling rules and artificial intelligence technology has become a hot research relevent to scheduling. Based on knowledge base's scheduling is Optimized to achieve the establishment of Knowledge in a group. How to resolve the dynamic scheduling problem in orer to make an product plan efficient is the primary problem in the scheduling research field. With development of market economy, many orders of multi-process and small-batch become the focus of the market which manufactory racing to acquire. In this way, manufactories should be asked to arrange sequences rationally, take advantage of resource, shorten time limit for a project and reduce cost of producing. Introducing the optimization theory to the field of job scheduling in workshop can improve the performance of algorithm, make the algorithm apply to broader fields and complete the whole system of the algorithm, which is a subject including both theoretical meaning and practical values.Adaptive Genetic Algorithm for solving job-shop scheduling problems has the defects of that the speed of convergence is slow on the early stage and it is easy to get local optimal solutions, this paper induce a time operator depending on the time evolution to solve this problem.Thereby overcoming the defect that adaptive Genetic Algorithm crossover and mutation probability can not make a corresponding adjustment with evolutionary time.Algorithm's structure is hierarchical, scheduling problems can be fully embodied the characteristics by using this strategy , not only improve the convergence rate but also maintain the diversity of the population, furthermore avoid premature.The populations in the same layer evolve with two goals-time optimal and cost optimal at the same time,the basic Genetic Algorithm is applicated between layers.In this thesis we study the scheduling theory and its development in a systemical way, and present a solution scheme which has complete theory, reliable practical foundation and high feasibility, to solve the production control problem in some manufactural enterprise, and then we designed and implemented an intelligent scheduling knowledge base system platform for workshop. Furtherly studying many intelligent search algorithm models. The improved algorithm was tested by Muth and Thompson benchmarks,the results show that the optimization algorithm is highly efficient and improves both the quality of solutions and speed of convergence..The improved algorithm is applied to actual problem and the result is feasible and effective.