Research On Shop Dynamic Scheduling Approaches Based On Improved Gene Expression Programming

With the vigorous development of science and technology and economy,shop scheduling has gradually become one of the key factors which affecting the production efficiency of manufacturing enterprises.Due to the endless emergencies in the actual scheduling,how to conduct efficient dynamic scheduling has become a key concern of enterprises.Gene Expression Programming(GEP)is a widely used evolutionary algorithm,but it is seldom used in scheduling problems.Based on the GEP algorithm,this paper deeply investigates the dynamic shop scheduling method.Firstly,the standard GEP algorithm is analyzed and studied.Based on this,the variable neighborhood search algorithm is embedded into it,and a variety of neighborhood structures are designed.An adaptive genetic operator is proposed to improve the performance of the algorithm.Based on the improved GEP algorithm,a dynamic shop scheduling framework is proposed.Secondly,the dynamic job shop scheduling problem is studied by taking the random arrival of jobs as dynamic events.The problem model is established,and the codec method for the problem is designed.A dynamic job shop scheduling rule construction method based on improved GEP is proposed.The simulation results show the effectiveness of the proposed method compared with GEP and GP algorithms.Then,the dynamic flexible job shop scheduling problem is studied and the job preparation time is considered.The mathematical model of the problem is established and the corresponding coding method is designed.Simulation experiments are designed and compared with GEP,GP and classical scheduling rules to verify the performance of the proposed method.Then,the multi-objective dynamic flexible job shop scheduling problem is studied.A MOGEP algorithm based on fast non-dominant scheduling is proposed.The MOGEP algorithm is compared with the commonly used nsga-ii algorithm,and its performance is verified through experiments.Finally,the thesis is summarized and the future research direction is prospected.