Scheduling Knowledge Reasoning Of Dynamic Job Shop And Knowledge System Design

In the modern production mode of manufacturing,the static scheduling scheme has been unable to adapt to the changing environment of job shop.The current scheduling knowledge system is based on the historical experience of experts,and it has many problems,such as the strong subjectivity,the partial attribute of decision making,the conflict of multiple sources of knowledge,the lag of knowledge and so on.Aiming at finding dynamic scheduling choice rules in the environment of uncertain job shop,try to make full use of production scheduling data of systems in manufacturing enterprises,use the genetic algorithm and BP algorithm to construct dynamic scheduling knowledge base,and design dynamic scheduling based on network knowledge system.First of all,establish the static mathematical model for the general job shop.Introduce encoding,decoding,crossover and mutation operation of genetic algorithm in solve job shop scheduling problem,and obtain the optimal solution of the problem.Summarize genetic algorithm operation in solving static flow shop scheduling problem.Secondly,analysis scheduling rules and several common composite dispatching rules,determine the direction of this study is to improve the adaptive scheduling;using genetic algorithm to solve 4x3 optimal scheduling problem.Based on the optimal solution of the problem to set network input and output parameters of BP algorithm.Find time conflict points of optimal solution decision by the genetic algorithm to calculate the artificial neural network input and output,to obtain training samples.of nonlinear network.Finally,analyze the background and key technology to establish scheduling knowledge system,By system requirement analysis of scheduling knowledge system to summarize the business process of SKS.put forward the system framework,key database design and software module design of system parts.