Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges
AC Huang, SH Meng, TJ Huang - Cluster Computing, 2023 - Springer
The technology of big data analysis and artificial intelligence deep learning has been
actively cross-combined with various fields to increase the effect of its original low single …
actively cross-combined with various fields to increase the effect of its original low single …
General purpose digital twin framework using digital shadow and distributed system concepts
Digital twin (DT) is an emerging concept in the Industry 4.0 era. It integrates intelligence into
industrial processes. The broadness of DT's concept allows for multiple definitions and …
industrial processes. The broadness of DT's concept allows for multiple definitions and …
Designing an adaptive and deep learning based control framework for modular production systems
In today's rapidly changing production landscape with increasingly complex manufacturing
processes and shortening product life cycles, a company's competitiveness depends on its …
processes and shortening product life cycles, a company's competitiveness depends on its …
Production-level artificial intelligence applications in semiconductor supply chains
This is a panel paper that discusses the use of Artificial Intelligence (AI) technologies to
address production and supply chain level problems in semiconductor manufacturing. We …
address production and supply chain level problems in semiconductor manufacturing. We …
[HTML][HTML] schlably: A Python framework for deep reinforcement learning based scheduling experiments
Research on deep reinforcement learning (DRL) based production scheduling (PS) has
gained a lot of attention in recent years, primarily due to the high demand for optimizing …
gained a lot of attention in recent years, primarily due to the high demand for optimizing …
Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey
Machine scheduling problems associated with semiconductor manufacturing operations
(SMOs) are one of the major research topics in the scheduling literature. Lots of papers have …
(SMOs) are one of the major research topics in the scheduling literature. Lots of papers have …
Comparison of Reinforcement Learning Methods for Production Control in Discrete Manufacturing Systems
The widespread deployment of smart meters and communication technologies brings
opportunities to improve the adaptability and flexibility of future manufacturing systems …
opportunities to improve the adaptability and flexibility of future manufacturing systems …
[PDF][PDF] Reinforcement learning applications in manufacturing
S Belli - 2023 - webthesis.biblio.polito.it
In the landscape of modern manufacturing, Reinforcement Learning (RL) stands out as a
promising frontier, offering transformative solutions to different challenges. This thesis …
promising frontier, offering transformative solutions to different challenges. This thesis …
[PDF][PDF] Deep Reinforcement Learning for Workload Balance and Due Date Control in Wafer Fabs
F Bergmann, S und Straßburger - db-thueringen.de
Semiconductor wafer fabrication facilities (wafer fabs) often prioritize two operational
objectives: work-in-process (WIP) and due date. WIP-oriented and due date-oriented …
objectives: work-in-process (WIP) and due date. WIP-oriented and due date-oriented …