Study On Optimization Of Mixed Flow Production Scheduling For Prefabricated Prefabricated Components Under Uncertain Environment

In order to promote the progress of green building development,several policies regarding the advancement of prefabricated building development have been issued by the state.,and vigorously promote prefabricated building.However,there are some problems in the production stage of prefabricated components,such as low standardization level and unreasonable production scheduling,which lead to low production efficiency of prefabricated components.In order to improve the production efficiency of precast components,most manufacturers have switched from a single production line to a multi-line mixed-flow production mode.Due to the expensive equipment of a specific process,resource preemption will occur in the process of a specific process,and uncertain situations such as uneven allocation of resources,machine failure and temporary shortage of manpower occur frequently in the production process.Hence,giving consideration to prefabricated parts during the production scheduling process holds immense value in achieving a rational distribution of production line resources,preventing disruptions caused by unpredictable factors in scheduling plans,and devising adaptable production plans to heighten the efficiency of prefabricated component production.In this paper,related processes,production lines and production modes of prefabricated prefabricated component production supply chain were analyzed first,and workshop production scheduling theory,robust scheduling theory and multi-objective theory were summarized.On this basis,an optimization model of prefabricated prefabricated component mixed-flow production scheduling under uncertain environment was established.In order to be closer to the actual production process,the production process,production line resources and mold resources were taken as constraint conditions,the penalty cost caused by advance or delay was minimized and the production line utilization rate was maximized under the multi-production line mode as the goal,and a deterministic model with production resources and production process time of prefabricated components as constraints,the model was added.Based on the objective function of scheduling robustness maximization,establishing a scheduling robust optimization model.Then,on the basis of the characteristics of process certainty,complicacy and finiteness production resources in the production scheduling of prefabricated components,based on the rules of Living of genetic algorithm survival of the fittest,the elite reverse learning strategy and adaptive cross mutation operator were introduced.An adaptive elite reverse genetic algorithm was designed for prefabricated components.By employing the multi-objective test functions for mathematical experiments,and comparing it with the current mainstream intelligent algorithms.Finally,the paper collected the relevant order data of a prefabricated building project through field research,applied the model and algorithm are suitable for actual production order,and carried out simulation with MATLAB.By comparing the single-machine scheduling model,robust optimization model are determined,the production line under the multi-production line mode,reduces the penalty cost caused by early or delayed delivery,and improves the anti-risk ability of the scheduling scheme faced with uncertainty,which verifies the rationality and validity of the model.It proves that EOBl-AGA has fast running speed and good distribution and convergence accuracy in solving this kind of problem.The research results of this paper provide a solution for solving prefabricated precast mixedflow production with scheduling problem.Aiming at the interference effect of uncertain factors of production environment on actual production scheduling of workshop,The actual production of actual prefabricters is mixed flow production management and actual production.The prefabricated building prefabricated.It expedites the process of building industrialization.