Deep reinforcement learning in smart manufacturing: A review and prospects

CIRP journal of manufacturing science and technology, Journal Year: 2022, Volume and Issue: 40, P. 75 - 101

Published: Dec. 2, 2022

Language: Английский

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Integration of deep reinforcement learning and multi-agent system for dynamic scheduling of re-entrant hybrid flow shop considering worker fatigue and skill levels

Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 84, P. 102605 - 102605

Published: June 16, 2023

Language: Английский

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DeepMAG: Deep reinforcement learning with multi-agent graphs for flexible job shop scheduling

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 259, P. 110083 - 110083

Published: Nov. 3, 2022

Language: Английский

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Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning

Advanced Engineering Informatics, Journal Year: 2022, Volume and Issue: 54, P. 101776 - 101776

Published: Oct. 1, 2022

Language: Английский

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High-accuracy prediction and compensation of industrial robot stiffness deformation

International Journal of Mechanical Sciences, Journal Year: 2022, Volume and Issue: 233, P. 107638 - 107638

Published: Aug. 17, 2022

Language: Английский

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Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell

IEEE Robotics and Automation Letters, Journal Year: 2022, Volume and Issue: 7(3), P. 7684 - 7691

Published: June 20, 2022

As industry rapidly shifts towards mass personalisation, the need for a decentralised multi-agent system capable of dynamic flexible job shop scheduling (FJSP) is evident. Traditional heuristic and meta-heuristic methods cannot achieve satisfactory results have limited application to static environments. Recent Reinforcement Learning (RL) approaches that consider FJSP, lack flexibility autonomy as they use single-agent centralised model, assuming global observability. such, we propose Multi-Agent (MARL) dynamically arriving assembly jobs in robot cell. We applied Double DQN-based algorithm proposed generalised observation, action reward design FJSP setting. Using training phase, each agent (i.e., robot) cell executes decisions based on local observations. Our solution demonstrated improved performance against rule-based methods, optimising makespan. also reported impact different observation sizes optimisation performance.

Language: Английский

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Cloud–edge collaboration task scheduling in cloud manufacturing: An attention-based deep reinforcement learning approach

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 177, P. 109053 - 109053

Published: Feb. 1, 2023

Language: Английский

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Collaborative dynamic scheduling in a self-organizing manufacturing system using multi-agent reinforcement learning

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102646 - 102646

Published: June 26, 2024

Language: Английский

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

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110856 - 110856

Published: Jan. 1, 2025

Language: Английский

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Coordinating dynamic signage for evacuation guidance: A multi-agent reinforcement learning approach integrating mesoscopic crowd modeling and fire propagation

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 194, P. 116246 - 116246

Published: March 9, 2025

Language: Английский

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