scholarly journals Correction to: Exploring the application of machine learning to the assembly line feeding problem

2021 ◽  
Author(s):  
Emilio Moretti ◽  
Elena Tappia ◽  
Veronique Limère ◽  
Marco Melacini
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Feeding Problem

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

2021 ◽  
Author(s):  
Moretti Emilio ◽  
Tappia Elena ◽  
Limère Veronique ◽  
Melacini Marco
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Optimal Solution ◽  
Product Variety ◽  
Supervised Machine Learning ◽  
Assembly System ◽  
Optimization Approach ◽  
Feeding Problem ◽  
Managerial Implications ◽  
Multiple Feeding

AbstractAs 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.

Image-Based Defect Detection in Assembly Line with Machine Learning

2021 ◽  
Author(s):  
Giovanni Burresi ◽  
Martino Lorusso ◽  
Lisa Graziani ◽  
Alice Comacchio ◽  
Federico Trotta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
Assembly Line

The assembly line feeding problem: An extended formulation with multiple line feeding policies and a case study

2020 ◽  
Vol 222 ◽  
pp. 107489
Author(s):  
Reinhard Baller ◽  
Steffen Hage ◽  
Pirmin Fontaine ◽  
Stefan Spinler
Keyword(s):  
Assembly Line ◽  
Extended Formulation ◽  
Feeding Problem ◽  
Multiple Line

Explainable Machine Learning to Improve Assembly Line Automation

2021 ◽  
Author(s):  
Sharmin Sultana Sheuly ◽  
Mobyen Uddin Ahmed ◽  
Shahina Begum ◽  
Michael Osbakk
Keyword(s):  
Machine Learning ◽  
Assembly Line

A Joint Assembly Line Balancing and Feeding Problem (JALBFP) considering direct and indirect supply strategies

2021 ◽  
pp. 1-19
Author(s):  
Martina Calzavara ◽  
Serena Finco ◽  
Daria Battini ◽  
Fabio Sgarbossa ◽  
Alessandro Persona
Keyword(s):  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Feeding Problem ◽  
Joint Assembly

scholarly journals A human-machine learning curve for stochastic assembly line balancing problems

2018 ◽  
Vol 51 (11) ◽  
pp. 1186-1191 ◽  
Author(s):  
F. Lolli ◽  
E. Balugani ◽  
R. Gamberini ◽  
B. Rimini ◽  
V. Rossi
Keyword(s):  
Machine Learning ◽  
Learning Curve ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Stochastic Assembly

scholarly journals The Nooscope manifested: AI as instrument of knowledge extractivism

AI & Society ◽  
2020 ◽  
Author(s):  
Matteo Pasquinelli ◽  
Vladan Joler
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Assembly Line ◽  
Learning Algorithm ◽  
Machine Intelligence ◽  
Training Dataset ◽  
The Social ◽  
The World ◽  
History Of ◽  
Learning Data

AbstractSome enlightenment regarding the project to mechanise reason. The assembly line of machine learning: data, algorithm, model. The training dataset: the social origins of machine intelligence. The history of AI as the automation of perception. The learning algorithm: compressing the world into a statistical model. All models are wrong, but some are useful. World to vector: the society of classification and prediction bots. Faults of a statistical instrument: the undetection of the new. Adversarial intelligence vs. statistical intelligence: labour in the age of AI.

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