基于工位复杂性测度的随机型装配线平衡优化

Stochastic assembly line balancing based on workstation complexity measurement

针对装配生产中各种不确定因素引起操作时间波动,导致确定型装配线平衡方案失效的问题,提出一种操作时间不确定的装配线平衡方法。以操作时间的概率分布描述其波动情况,进而基于信息熵提出一种工位复杂性测度方法,在此基础上建立以平衡率最高、工位负荷均衡、工位复杂度均衡为目标的线平衡优化模型;设计了一种改进遗传算法对其进行求解,并通过实例验证模型及算法的有效性。研究结果表明,所提方法能在保证整线平衡率和工位负荷均衡的同时,均衡各工位复杂度,降低不确定性因素对生产过程的影响,保证装配线生产平稳运行。

Uncertain operation time made workstation time and cycle time fluctuate,which would probably lead to the failure of deterministic assembly line balancing scheme.Aiming at this problem,a balancing method was proposed for the stochastic assembly line.The operation time was described with its probability distributions,and then the workstation complexity measuring method was given based on information entropy.An optimization model was established by taking the maximum balancing rate,workstation load balancing and workstation complexity balancing as objectives.An improved genetic algorithm was put forward to solve the model,and an instance was used to verify the effectiveness of the proposed model and algorithm.The results illustrated that the method balanced the complexities of stations with the greatest productivity and balanced workstation load,which could reduce the effect of uncertainties on the line and make the production more stable and smooth.