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20222026
most citedDeep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

49 citations · 110 across the 10 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cs.LG2023★ 49 cited

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

Maziyar Khadivi, Todd Charter, Marjan Yaghoubi +4

Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. This optimization leads to reduced operational costs,…

cs.CV2023★ 16 cited

Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations

Assef Ghamisi, Todd Charter, Li Ji +3

Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective…