Modeling And Simulation Of Stainless Steel Workshop Based On Modelica And Optimization Of Workshop Completion Time

With the development of information technology,competition in the steel industry has become increasingly fierce.By studying the modeling and capacity optimization of the stainless steel production workshop,and the rational use of processing equipment,the efficiency of the steel workshop can be improved,the production cost can be reduced,and its competitiveness can be enhanced.In recent years,customer orders have changed from a single-variety,largevolume demand mode to a multi-variety,small-batch demand mode.Enterprises should also change the processing mode to cope with this change.This article builds a simulation model for the stainless steel production workshop under this processing mode.Carry out modeling and simulation,and optimize the completion time of the workshop.First,by analyzing the process flow of the stainless steel production workshop,combining the hierarchical modeling idea to divide the basic functional units of the component model,and defining the functional indicators such as model interface and model parameters based on the object-oriented idea,constructing the framework of each basic component model.On this basis,the Modelica modeling language is used to construct the component model of the stainless steel production workshop based on the finite state machine theory,realize the internal logic of each state and complete the construction of the stainless steel production workshop component model.The use of components to build a production case for simulation verifies the effectiveness of the component model constructed in this article.Secondly,taking a stainless steel production workshop as the research object,based on the constructed component model,the system simulation model under the two processing modes is built.The pros and cons of the two parallel machine selection modes of the sorting conveyor belt model are compared.The results show that the parallel machine selection mode of weighted polling can give full play to the performance of the parallel machine and improve the production efficiency.The weight of each parallel machine in the parallel processing stage is determined by simulation.The system simulation models of the two processing modes were simulated separately,and indicators such as the maximum total completion time,the utilization rate of the buffer at each stage,and the workshop balance were analyzed,and it was shown that the processing mode that allows the workpiece to skip steps that it has not to be processed can effectively improve the production efficiency and balance.Finally,aiming at the defect that the particle swarm algorithm is easy to fall into the local optimal solution prematurely,an improved particle swarm algorithm is proposed,which increases the search range by combining genetic algorithm crossover operations and expanding the number of candidate optimal particles.Taking the minimization of the maximum total completion time as the optimization goal,the algorithm before and after the improvement is used to optimize the model based on the built model.The results show that the improved algorithm can effectively obtain a better solution.At the same time,the Taillard standard test case is used to simulate and verify the proposed algorithm.By comparing with the results of existing research algorithms,the effectiveness of the proposed algorithm on problems of different scales is verified.