The Research On Flowshop Scheduling Simulation Optimization Of Production System Based On Genetic Algorithm

With the advent of smart manufacturing,the rapid economic development and more and more mature manufacturing technologies,the competition in the manufacturing industry has become a competition between speed and efficiency.Therefore,modern manufacturing companies need to pass on production resources.The rationalization of configuration and efforts to improve the production system in daily production activities have made it difficult to pass mathematical calculations because of the optimized needs and effects,the actual implementation has been overly costly,and the selection of optimization measures has also become more complicated.This paper establishes a simulation optimization of the production system.The model is used to better optimize the production system and measure the optimization effect.This article first summarizes some domestic and foreign scholars' research on the flowshop problem,and analyzes commonly used methods in production scheduling and common algorithms for solving scheduling problems.With the advent of the artificial intelligence era,the flowshop problem for production systems is also solved by the general algorithm.Gradually meta-heuristic development,followed by a summary of domestic and foreign research on production system simulation and optimization,introduced some of the more commonly used production system simulation software,and production system optimization measures,established a generic simulation of flowshop problem solving in production systems Optimize the model,use flexsim simulation software to simulate the operation of the production system,establish the genetic algorithm model,and use the matlab modeling software to implement the genetic algorithm model.According to the results of the operation,the optimization measures are obtained,and then the simulation software is used to verify the optimization measures.If the optimization measures are effective and reasonable,they can be implemented.If the optimization measures prove to be unreasonable,the optimization measures need to be adjusted or re-examined.Finally,the model was applied practically,and the operation of the vehicle assembly line of Y Company was analyzed.After the optimization requirements were determined,an improved genetic algorithm model was established.Through the analysis of the results,the validity,rationality and integrity of the model were verified,and the basic The optimization results of the genetic algorithm model are compared.The traditional system optimization research verification method is simpler,and the validation goal is also relatively simple.This paper selected a simulation software that can highly simulate the production system,improved the genetic algorithm,improved the optimization efficiency,and considered the optimization measures when verifying the results.The impact of other parts of the system,if the optimization measures do not create new bottlenecks in other parts of the production system and do not affect system integrity,the optimization measures are reasonable.The following conclusions are drawn from this study:(1)The evaluation indicators of the production system include the production cycle,bottleneck rate,processing time,and net output.The results of the production line are obtained through the simulation results of the simulation model created by the flexsim simulation software.It can be applied to the operation of other types of production lines and has a wide range of applications.(2)This paper applies the generation gap mechanism,namely the elite preservation strategy,and the adaptive strategy genetic algorithm model,which not only preserves the powerful global search ability of the traditional genetic algorithm,but also enhances the local search ability,overcoming the phenomenon of premature maturity in the early stages of evolution.,and improve the lack of convergence at the end of evolution,improving the overall performance of the algorithm.(3)Verify the effectiveness and rationality of the optimization measures,and study the impact of the optimization measures on the other stations of the production line so as to avoid partial optimization and reduce overall efficiency.This study has the following limitations:(1)This article has not completely overcome all the shortcomings of the genetic algorithm,and the research in coding is not enough.(2)The optimization of the production line by the genetic algorithm can only schedule the production sequence,workshop layout,etc.,but does not involve the production process and more complex optimization of the production line.In the future research of the production line,the production process should be studied to reduce the number of The production process time,while ensuring the quality of production,improve production efficiency from all aspects.