Research On Intelligent Scheduling Algorithm Based On Neural Network Deep Learning

With the gradual rise of customized manufacturing,intelligent manufacturing systems have been widely used,and the accompanying intelligent scheduling problem has become a research hotspot.In particular,the proposal of Industry 4.0 makes intelligent scheduling play the role of the core brain in the intelligent factory and intelligent logistics of intelligent manufacturing,and is the basis of intelligent manufacturing.As one of the most important factors,job shop scheduling is a key link that affects the output of the manufacturing industry.Today's scholars generally focus on the improvement of scheduling algorithm and further exploration of scheduling's own attributes.Since the 21 st century,the research on these two methods has focused on the research of solving algorithms.In short,combining several different algorithms to solve the job shop scheduling problem is the main direction of research today.The mainstream method of current research is to solve many algorithms and neural networks in different combinations.Deep learning neural networks are superior to traditional shallow neural networks in handling problems with large amounts of historical data and more complex data types.The genetic algorithm has strong global search ability and robustness,and is suitable for solving complex optimization problems.Based on the advantages of genetic algorithm and neural network deep learning,it is proposed to use genetic algorithm optimized by deep learning neural network for job shop intelligent scheduling,and research is conducted on flexible job shop intelligent scheduling problem with cable workshop as the research object.The following is the work content and innovation of this article:(1)By analyzing the production methods of cable products and the characteristics of cable workshop production problems,a mathematical model of workshop scheduling problems is established.The core research content of this paper is established based on the research on the various flexibility of related product production workshops and the diversity of scheduling problems: designing an intelligent scheduling based on the cable workshop and can be applied to solve various types of flexible job shop scheduling problems method.(2)This paper solves the actual situation of production scheduling problem based on genetic algorithm,combined with the mathematical model of constructing a cable production workshop in this paper,designed a genetic algorithm suitable for cable production workshop,according to the multi-objective flexible scheduling operation workshop Features The scheduling target is set to the problems of the shortest maximum completion time and the smallest processing cost,etc.,and is solved using an improved genetic algorithm to establish a workshop intelligent scheduling model based on the improved genetic algorithm.(3)Through the application of deep learning neural network algorithm,long-term and short-term memory neural network to the production data prediction of cable companies,the responsiveness of scheduling to the actual production environment is enhanced,and through deep machine learning of a large number of production scheduling data,the construction The production scheduling data prediction model provides great decision support for dynamic scheduling,and also lays the foundation for the optimization of the fitness function of the subsequent genetic algorithm to solve the intelligent scheduling problem in the flexible job shop.(4)Combining the actual needs of the cable workshop and the scheduling method proposed in this paper,an intelligent scheduling algorithm combining deep learning neural network and genetic algorithm is designed,and a workshop scheduling model based on genetic algorithm is established,using deep learning neural The network improved the genetic algorithm,designed a new fitness function construction method,optimized the problem of local optimization in the traditional genetic algorithm,and improved the global search performance.