A review on learning to solve combinatorial optimisation problems in manufacturing

C Zhang, Y Wu, Y Ma, W Song, Z Le… - IET Collaborative …, 2023 - Wiley Online Library
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 …

[HTML][HTML] Graph neural networks for job shop scheduling problems: A survey

IG Smit, J Zhou, R Reijnen, Y Wu, J Chen… - Computers & Operations …, 2024 - Elsevier
Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …

Large-scale dynamic scheduling for flexible job-shop with random arrivals of new jobs by hierarchical reinforcement learning

K Lei, P Guo, Y Wang, J Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …

Solving flexible job shop scheduling problems via deep reinforcement learning

E Yuan, L Wang, S Cheng, S Song, W Fan… - Expert Systems with …, 2024 - Elsevier
Flexible job shop scheduling problem (FJSSP), as a variant of the job shop scheduling
problem, has a larger solution space. Researchers are always looking for good methods to …

Flexible job shop scheduling via dual attention network-based reinforcement learning

R Wang, G Wang, J Sun, F Deng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Flexible manufacturing has given rise to complex scheduling problems such as the flexible
job shop scheduling problem (FJSP). In FJSP, operations can be processed on multiple …

Manufacturing in the age of human-centric and sustainable industry 5.0: application to holonic, flexible, reconfigurable and smart manufacturing systems

C Turner, J Oyekan - Sustainability, 2023 - mdpi.com
This paper provides a classification of manufacturing types in terms of new technological
tools provided in the Industry 5.0 framework. The manufacturing types agile, holonic, flexible …

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 …

Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV

F Zhang, R Li, W Gong - Computers & Industrial Engineering, 2024 - Elsevier
The integration of manufacturing and logistics scheduling issues in shop operations has
garnered considerable attention. Concurrently, escalating concerns about global warming …

A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time

X Wu, X Yan, D Guan, M Wei - Engineering applications of artificial …, 2024 - Elsevier
The dynamic job-shop scheduling problem (DJSP) is a type of scheduling tasks where
rescheduling is performed when encountering the uncertainties such as the uncertain …

[HTML][HTML] Job shop smart manufacturing scheduling by deep reinforcement learning

JC Serrano-Ruiz, J Mula, R Poler - Journal of Industrial Information …, 2024 - Elsevier
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 …