Multi-objective Optimization Design And Application Research Of Flexible Workshop Scheduling

Manufacturing is the pillar industry of China's national economy.Intelligent manufacturing such as intelligent equipment and smart factories based on information physics systems is leading the transformation of manufacturing methods.Workshop scheduling is a vital part of manufacturing enterprise workshop production management.When manufacturing enterprises face complex and dynamic complex dynamic environments constrained by production resources,if they can reasonably make scientific decisions on production scheduling,quickly respond to the market dynamically.The demand can not only improve equipment utilization,reduce product manufacturing cycle,but also improve product quality and reduce production costs.Therefore,how to use computer technology to achieve production scheduling optimization,rapid adjustment of resource allocation,overall scheduling of production schedules,and improved equipment utilization have become major issues facing many processing companies.Based on the existing research results,through the understanding of the background and development status of the workshop scheduling,this paper starts with the workshop scheduling theory and the theoretical theory of the algorithms used to study the production cost,the processing time of the parts in the machine and the advance/delay.Aiming at the impact of completion on the production chain,a multi-objective flexible workshop scheduling model is constructed,and a production scheduling scheme is established with a processing instance as the research object.The work done in this paper is:(1)Through the research of genetic algorithm and particle swarm algorithm,the general steps of algorithm design are mastered,and the application of two basic algorithms in shop scheduling is explored.Combined with the production example of the workshop,the workpiece production information is used for scheduling simulation using MATLAB.(2)Through the research on the theory of shop scheduling theory,a multi-objective shop scheduling model with parts manufacturing cycle,advanced/delayed delivery and processing cost is established.The genetic algorithm is used to analyze the model.The optimization direction of the objective function is the direction of the fitness value increase.Compared with the experimental results in the reference literature,the usability of the constructed model and the goodness of the algorithm are verified.(3)According to the established multi-objective scheduling model,use the command window,editor and debugger module in MATLAB tool,and combine different production algorithms to optimize it.Introducing the non-dominated genetic algorithm of elite retention strategy,performing non-dominated sorting stratification and fitness value calculation on the population,and using niche technology to ensure the distribution of Pareto optimal solution set;using MPSO algorithm with meshing,The pressure parameters are set to select the Pareto set.Combining the advantages of the genetic algorithm and the particle swarm optimization algorithm,the genetic algorithm operation is performed on the population after the particle group optimization to output the Pareto optimal solution set.The different characteristics of the above algorithm are analyzed.The simulation results are used to compare the advantages and disadvantages of the algorithm in solving the problem,which can play a guiding role in actual production.