Exploring the application of machine learning to the assembly line feeding problem

Emilio Moretti (), Elena Tappia, Veronique Limère and Marco Melacini
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Emilio Moretti: Politecnico Di Milano
Elena Tappia: Politecnico Di Milano
Veronique Limère: Ghent University
Marco Melacini: Politecnico Di Milano

Operations Management Research, 2021, vol. 14, issue 3, No 11, 403-419

Abstract: Abstract As a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

Keywords: Assembly system; Part feeding; Factory logistics; Optimization; Machine learning; End-to-end learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12063-021-00201-3

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