Research On Modeling And Intelligent Optimization Of Shared Cellular Manufacturing System In Distributed Factory

With the upgrading of consumption,traditional cell manufacturing has been unable to meet consumers’ personalized needs for products;The rise of new intelligent manufacturing technologies in China,such as digital twin technology and collaborative manufacturing,also gave birth to a more flexible cell manufacturing mode.Sharing manufacturing cells can improve the utilization rate of machines and the flexibility of manufacturing cells,thus helping manufacturing enterprises to carry out multi variety small-scale batch production more efficiently,so as to achieve cost reduction and efficiency increase.At the same time,due to the epidemic prevention and control,China’s automobile industry is facing a huge crisis this year,which highlights the possible risks of China’s industrial agglomeration.Distributed production can undoubtedly effectively enhance the stability of the supply chain of manufacturing enterprises.This paper studies the manufacturing optimization of shared cells in distributed factories,mainly from the following two aspects:(1)Virtual shared manufacturing cell system.In order to meet the needs of the era of mass customization,this research proposes a dynamic virtual shared manufacturing cell model based on product family clustering,flexible allocation of manufacturing cell capacity based on product demand,to reduce machine configuration and energy consumption,improve machine utilization,and reduce inventory;So as to help enterprises to allocate existing resources more reasonably and minimize the manufacturing costs of enterprises.For this reason,a damped vibration algorithm based on genetic algorithm(GAVDO)is designed and compared with the classical damped vibration optimization algorithm(VDO)and genetic algorithm(GA).Through large-scale numerical experiments,the results show that GAVDO is superior to VDO and GA in the same running time;At the same time,it is proved that shared manufacturing cell has obvious cost advantage compared with non shared manufacturing cell.(2)Micro shared manufacturing cell system.By reasonably dividing the processing procedures and combining the machines into micro units,this research aims to minimize the production cost and completion time,and constructs an integrated system for micro shared unit construction and production scheduling in distributed factories to help manufacturing enterprises meet the differentiated needs of multiple markets and reduce production costs while ensuring the output.Therefore,a multi-objective particle swarm optimization algorithm(MOPSO)is designed to solve the model to determine the shared cell construction and production scheduling scheme,and it is compared with the non dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).Large scale numerical experiments show that MOPSO is superior to NSGA-Ⅱ in the same running time.