Deep reinforcement learning in production systems: A systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Machine learning-supported manufacturing: A review and directions for future research
The evolution of manufacturing systems toward Industry 4.0 and 5.0 paradigms has pushed
the diffusion of Machine Learning (ML) in this field. As the number of articles using ML to …
the diffusion of Machine Learning (ML) in this field. As the number of articles using ML to …
Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning
S Luo, L Zhang, Y Fan - Computers & Industrial Engineering, 2021 - Elsevier
In modern volatile and complex manufacturing environment, dynamic events such as new
job insertions and machine breakdowns may randomly occur at any time and different …
job insertions and machine breakdowns may randomly occur at any time and different …
Multi-objective reinforcement learning framework for dynamic flexible job shop scheduling problem with uncertain events
H Wang, J Cheng, C Liu, Y Zhang, S Hu, L Chen - Applied Soft Computing, 2022 - Elsevier
The economic benefits for manufacturing companies will be influenced by how it handles
potential dynamic events and performs multi-objective real-time scheduling for existing …
potential dynamic events and performs multi-objective real-time scheduling for existing …
Cognitive intelligence in industrial robots and manufacturing
The transition from manual to autonomous manufacturing processes, which has been
propelled by consecutive industrial revolutions, is concurrently contingent upon …
propelled by consecutive industrial revolutions, is concurrently contingent upon …
Opportunistic maintenance scheduling with deep reinforcement learning
The great complexity of advanced manufacturing processes combined with the high
investment costs for manufacturing equipment makes the integration of maintenance …
investment costs for manufacturing equipment makes the integration of maintenance …
[HTML][HTML] Job shop smart manufacturing scheduling by deep reinforcement learning
Smart manufacturing scheduling (SMS) requires a high degree of flexibility to successfully
cope with changes in operational decision level planning processes in today's production …
cope with changes in operational decision level planning processes in today's production …
Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning
Z Müller-Zhang, T Kuhn, PO Antonino - Computers in Industry, 2023 - Elsevier
Production flow is becoming increasingly complex since manufacturers must react quickly to
changing markets demands and diverse customer requirements. In order to ensure …
changing markets demands and diverse customer requirements. In order to ensure …
Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning
M Wurster, M Michel, MC May, A Kuhnle… - Journal of intelligent …, 2022 - Springer
Remanufacturing includes disassembly and reassembly of used products to save natural
resources and reduce emissions. While assembly is widely understood in the field of …
resources and reduce emissions. While assembly is widely understood in the field of …
Artificial intelligence and advanced materials in automotive industry: Potential applications and perspectives
The first part of this paper presents a literature review-based study of the most emerging
technology of the century, artificial intelligence (AI) and its various applications in the …
technology of the century, artificial intelligence (AI) and its various applications in the …