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 …
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 …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network
Y Li, W Gu, M Yuan, Y Tang - Robotics and Computer-Integrated …, 2022 - Elsevier
With the extensive application of automated guided vehicles in manufacturing system,
production scheduling considering limited transportation resources becomes a difficult …
production scheduling considering limited transportation resources becomes a difficult …
A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times
Y Du, J Li, C Li, P Duan - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …
significantly improve the energy, cost, and time efficiency of production. As one type of …
Deep learning applications in manufacturing operations: a review of trends and ways forward
Deep learning applications in manufacturing operations: a review of trends and ways forward |
Emerald Insight Books and journals Case studies Expert Briefings Open Access Publish with …
Emerald Insight Books and journals Case studies Expert Briefings Open Access Publish with …
Reinforcement learning for facilitating human-robot-interaction in manufacturing
For many contemporary manufacturing processes, autonomous robotic operators have
become ubiquitous. Despite this, the number of human operators within these processes …
become ubiquitous. Despite this, the number of human operators within these processes …
Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival
J Chang, D Yu, Y Hu, W He, H Yu - Processes, 2022 - mdpi.com
The production process of a smart factory is complex and dynamic. As the core of
manufacturing management, the research into the flexible job shop scheduling problem …
manufacturing management, the research into the flexible job shop scheduling problem …
Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling
As a new analogy paradigm of human learning process, reinforcement learning (RL) has
become an emerging topic in computational intelligence (CI). The synergy between the RL …
become an emerging topic in computational intelligence (CI). The synergy between the RL …