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

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...

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Bibliographic Details
Published in:Operations management research 2021-12, Vol.14 (3-4), p.403-419
Main Authors: Moretti, Emilio, Tappia, Elena, Limère, Veronique, Melacini, Marco
Format: Article
Language:English
Subjects:
Assembly lines
Backup software
Business and Management
Data collection
Engineering Economics
Industrial and Production Engineering
Innovation/Technology Management
Literature reviews
Logistics
Machine learning
Management
Marketing
Operations Management
Operations research
Operations Research/Decision Theory
Optimization
Organization
Policies
System effectiveness
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Summary: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.
ISSN:1936-9735
1936-9743
DOI:10.1007/s12063-021-00201-3