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 …
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 …
Deep learning applications in manufacturing operations: a review of trends and ways forward
Purpose Deep learning (DL) technologies assist manufacturers to manage their business
operations. This research aims to present state-of-the-art insights on the trends and ways …
operations. This research aims to present state-of-the-art insights on the trends and ways …
Reinforcement learning applied to production planning and control
The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …
(RL) techniques in the production planning and control (PPC) field addressing the following …
A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals
J Zhu, W Zhang, L Yu, X Guo - Omega, 2024 - Elsevier
To improve the operating efficiency of container terminals, we investigate a closed-loop
scheduling method in an autonomous inter-terminal system that employs unmanned …
scheduling method in an autonomous inter-terminal system that employs unmanned …
A decade of engineering-to-order (2010–2020): Progress and emerging themes
In 2009 a literature review on supply chain management in Engineer-to-Order (ETO)
situations was published in the International Journal of Production Economics (Gosling and …
situations was published in the International Journal of Production Economics (Gosling and …
Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems
L Kaven, P Huke, A Göppert, RH Schmitt - Journal of Intelligent …, 2024 - Springer
Manufacturing systems are undergoing systematic change facing the trade-off between the
customer's needs and the economic and ecological pressure. Especially assembly systems …
customer's needs and the economic and ecological pressure. Especially assembly systems …
Dynamic storage location assignment in warehouses using deep reinforcement learning
C Waubert de Puiseau, DT Nanfack, H Tercan… - Technologies, 2022 - mdpi.com
The warehousing industry is faced with increasing customer demands and growing global
competition. A major factor in the efficient operation of warehouses is the strategic storage …
competition. A major factor in the efficient operation of warehouses is the strategic storage …
A three-dimensional spatial resource-constrained project scheduling problem: Model and heuristic
J Zhang, L Li, E Demeulemeester, H Zhang - European Journal of …, 2024 - Elsevier
For a class of complex engineering projects executed in limited construction sites, spatial
resources with three dimensions usually become a bottleneck that hampers their smooth …
resources with three dimensions usually become a bottleneck that hampers their smooth …
An inspection network with dynamic feature extractor and task alignment head for steel surface defect
High-precision identification and real-time localization for irregular-shaped steel surface
defects are crucial for shipbuilding quality control. Although traditional lightweight networks …
defects are crucial for shipbuilding quality control. Although traditional lightweight networks …