Real-time scheduling for dynamic partial-no-wait multiobjective flexible job shop by deep reinforcement learning
S Luo, L Zhang, Y Fan - IEEE Transactions on Automation …, 2021 - ieeexplore.ieee.org
In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in
real time and each job may contain several special operations in partial-no-wait constraint …
real time and each job may contain several special operations in partial-no-wait constraint …
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 …
Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
S Luo - Applied Soft Computing, 2020 - Elsevier
In modern manufacturing industry, dynamic scheduling methods are urgently needed with
the sharp increase of uncertainty and complexity in production process. To this end, this …
the sharp increase of uncertainty and complexity in production process. To this end, this …
Dynamic scheduling for flexible job shop using a deep reinforcement learning approach
Y Gui, D Tang, H Zhu, Y Zhang, Z Zhang - Computers & Industrial …, 2023 - Elsevier
Due to the influence of dynamic changes in the manufacturing environment, a single
dispatching rule (SDR) cannot consistently attain better results than other rules for dynamic …
dispatching rule (SDR) cannot consistently attain better results than other rules for dynamic …
Deep reinforcement learning for dynamic flexible job shop scheduling problem considering variable processing times
L Zhang, Y Feng, Q Xiao, Y Xu, D Li, D Yang… - Journal of Manufacturing …, 2023 - Elsevier
In recent years, the uncertainties and complexity in the production process, due to the
boosted customized requirements, has dramatically increased the difficulties of Dynamic …
boosted customized requirements, has dramatically increased the difficulties of Dynamic …
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 …
Dynamic scheduling method for job-shop manufacturing systems by deep reinforcement learning with proximal policy optimization
With the rapid development of Industrial 4.0, the modern manufacturing system has been
experiencing profoundly digital transformation. The development of new technologies helps …
experiencing profoundly digital transformation. The development of new technologies helps …
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 …
Large-scale dynamic scheduling for flexible job-shop with random arrivals of new jobs by hierarchical reinforcement learning
As the intelligent manufacturing paradigm evolves, it is urgent to design a near real-time
decision-making framework for handling the uncertainty and complexity of production line …
decision-making framework for handling the uncertainty and complexity of production line …
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