Load balance optimization model of surface-mount production line considering uncertain production factors
Aiming at the problem that uncertain production factors cause a lag in order fulfillment for surface-mount manufacturers,a load balance optimization model of production line considering uncertain production factors is designed.First,the historical sample data of uncertain production factors are used as a random simulation sample to predict the lag time of order completion caused by uncertain production factors.Second,after optimizing the component placement workstation allocation scheme,the task completion is used as the trigger event to simulate the actual operation of the production line to obtain a dynamic production plan.Third,after calculating the model fitness value according to the dynamic production plan,the genetic algorithm is used to heuristically optimize the model fitness value to obtain the optimal dynamic production plan.Finally,the model is validated by using a surface-mount production line test case.The result shows that the model can accurately predict the production tasks,task volume and placement stations of components in each period of the production line and can effectively improve the production efficiency of enterprises.
load balancingMonte Carlo methodneural networksgenetic algorithm
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