A supervised machine learning approach for the optimisation of the assembly line feeding mode selection.

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Bibliographic Details
Title:	A supervised machine learning approach for the optimisation of the assembly line feeding mode selection.
Authors:	Zangaro, Francesco^1,2 (AUTHOR) francesco.zangaro@phd.unipd.it, Minner, Stefan¹ (AUTHOR), Battini, Daria² (AUTHOR)
Source:	International Journal of Production Research. Aug2021, Vol. 59 Issue 16, p4881-4902. 22p. 4 Diagrams, 9 Charts, 4 Graphs.
Subjects:	Supervised learning, Assembly line methods, Decision trees, Problem solving, Timberline
Abstract:	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%. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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%. [ABSTRACT FROM AUTHOR]
ISSN:	00207543
DOI:	10.1080/00207543.2020.1851793