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A supervised machine learning approach for the optimisation of the assembly line feeding mode selection
International Journal of Production Research ( IF 9.2 ) Pub Date : 2020-12-08
Francesco Zangaro, Stefan Minner, Daria Battini

The Line Feeding Problem (LFP) involves the delivery of components to the production area. Previous models minimise the delivery costs and optimally assign each component to a line feeding mode between line stocking, kitting, and sequencing but cannot provide easily comprehensible guidelines. We use the Classification And Regression Tree (CART) algorithm to develop, in a supervised way, a decision tree based on problems that are solved with a Mixed Integer Programming (MIP) model for training purposes. Based on selected attributes of the components and the manufacturing environment, the decision tree suggests a line feeding mode for every component. For a synthetically determined training and evaluation data set, we find that the classification tree can predict the line feeding mode with an average classification accuracy of 78.49%. After the decision tree is implemented and a line feeding mode is selected for each component, an infeasible solution might occur. We develop a repair approach that solves this problem with an average cost deviation from the optimal solution of 0.38%.



中文翻译:

一种有监督的机器学习方法,用于优化装配线进料模式选择

线进给问题(LFP)涉及将零件运送到生产区域。先前的模型可以最大程度地降低交付成本,并且可以在生产线进样,安装和排序之间将每个组件最佳地分配给生产线进给模式,但是无法提供易于理解的指导。我们使用分类和回归树(CART)算法以监督的方式开发基于基于混合整数编程(MIP)模型解决的问题的决策树,以进行培训。基于组件的选定属性和制造环境,决策树为每个组件建议换行模式。对于综合确定的训练和评估数据集,我们发现分类树可以预测换行模式,平均分类精度为78.49%。实施决策树并为每个组件选择换行模式后,可能会出现不可行的解决方案。我们开发了一种维修方法来解决此问题,平均成本与最佳解决方案的偏差为0.38%。

更新日期:2020-12-08
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