Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
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 …
Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning
T Lazebnik - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Hospital staff and resources allocation (HSRA) is a critical challenge in healthcare systems,
as it involves balancing the demands of patients, the availability of resources, and the need …
as it involves balancing the demands of patients, the availability of resources, and the need …
Optimal layout planning for human robot collaborative assembly systems and visualization through immersive technologies
The deployment of Industry 4.0 emerging technologies such as Augmented reality (AR),
Virtual reality (VR), and collaborative Robots enhances flexibility and precision in the …
Virtual reality (VR), and collaborative Robots enhances flexibility and precision in the …
Bridging evolutionary algorithms and reinforcement learning: A comprehensive survey on hybrid algorithms
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
[HTML][HTML] Graph neural networks for job shop scheduling problems: A survey
Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
Race: improve multi-agent reinforcement learning with representation asymmetry and collaborative evolution
Abstract Multi-Agent Reinforcement Learning (MARL) has demonstrated its effectiveness in
learning collaboration, but it often struggles with low-quality reward signals and high non …
learning collaboration, but it often struggles with low-quality reward signals and high non …
The application of heterogeneous graph neural network and deep reinforcement learning in hybrid flow shop scheduling problem
Y Zhao, X Luo, Y Zhang - Computers & Industrial Engineering, 2024 - Elsevier
The hybrid flow shop scheduling problem (HFSP) is a fundamental optimization problem in
the process industries. HFSP needs to consider both job selection and machine allocation …
the process industries. HFSP needs to consider both job selection and machine allocation …
Dynamic scheduling for flexible job shop with insufficient transportation resources via graph neural network and deep reinforcement learning
M Zhang, L Wang, F Qiu, X Liu - Computers & Industrial Engineering, 2023 - Elsevier
The smart workshop is a powerful tool for manufacturing companies to reduce waste and
improve production efficiency through real-time data analysis for self-organized production …
improve production efficiency through real-time data analysis for self-organized production …