Research On Flexible Job Shop Scheduling Algorithm Under Multi-Granularity Rules

Workshop scheduling is a core issue in implementing the strategy of manufacturing power and enhancing the international competitiveness of the manufacturing industry.The flexible job shop scheduling problem is a typical problem in the NP-hard problem,and is also a hot issue studied by industry and academia.In recent years,due to the development of technology,the study of traditional scheduling methods is facing huge challenges.At the same time,data-based scheduling methods are attracting the attention of industry and academia.Based on this,the paper makes the following research:First of all,the paper introduces the related research background and classification of workshop scheduling problems in detail.The paper analyzes the rule satisfaction of the decision points in the near-optimal scheduling scheme of the two standard data sets,and the rule_end rule has the highest usage rate.Secondly,the paper combines rules and genetic algorithms to efficiently solve flexible job shop scheduling problems.The paper optimizes the initial population of genetic algorithms through rules;improves the selection,crossover,and mutation operators of genetic algorithms;an external elite library based on rule updates is added to speed up the convergence of genetic algorithms;multiple constraints are added during decoding operations make model can not only solve the static scheduling problem,but also deal with the dynamic workshop scheduling problem,then prove the effectiveness of the algorithm to solve the workshop scheduling problem through experiments.Finally,the paper uses multi-granularity rules to solve flexible job shop scheduling problems.The paper regards the production scheduling process as a real-time dynamic decision-making process.On the problem of machine selection,through the analysis of historical scheduling data,the mapping relationship between the workshop status and rules is obtained,so that the scheduling decision is switched according to the workshop status,and verified the feasibility and efficiency of rule scheduling through experiments.