Graph neural networks in IoT: A survey
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …
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 …
Machine learning to solve vehicle routing problems: A survey
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
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 …
A review of combinatorial optimization problems in reverse logistics and remanufacturing for end-of-life products
Y Ren, X Lu, H Guo, Z **e, H Zhang, C Zhang - Mathematics, 2023 - mdpi.com
During the end-of-life (EOL) product recovery process, there are a series of combinatorial
optimization problems (COPs) that should be efficiently solved. These COPs generally result …
optimization problems (COPs) that should be efficiently solved. These COPs generally result …
Machine learning augmented approaches for hub location problems
Hub location problems are widely analyzed in fields of logistic and transportation industry for
cost reduction. In this paper, a novel algorithm framework based on machine learning is …
cost reduction. In this paper, a novel algorithm framework based on machine learning is …
Temporal metrics based aggregated graph convolution network for traffic forecasting
F Chen, Y Qi, J Wang, L Chen, Y Zhang, L Shi - Neurocomputing, 2023 - Elsevier
Traffic forecasting is one of the most well-studied problems in the Intelligent Transportation
Systems (ITS). However, existing studies mainly utilize Euclidean distance or road network …
Systems (ITS). However, existing studies mainly utilize Euclidean distance or road network …
Graph transformer with reinforcement learning for vehicle routing problem
G Fellek, A Farid, G Gebreyesus… - IEEJ Transactions on …, 2023 - Wiley Online Library
Vehicle routing problem (VRP) is one of the classic combinatorial optimization problems
where an optimal tour to visit customers is required with a minimum total cost in the …
where an optimal tour to visit customers is required with a minimum total cost in the …
Regulating the imbalance for the container relocation problem: A deep reinforcement learning approach
Y Tang, Z Ye, Y Chen, J Lu, S Huang… - Computers & Industrial …, 2024 - Elsevier
The main objective of the container relocation problem (CRP) is to retrieve all containers
stacked in a container terminal while following the required retrieval sequence and …
stacked in a container terminal while following the required retrieval sequence and …
Separable spatial-temporal residual graph for cloth-changing group re-identification
Group re-identification (GReID) aims to correctly associate group images belonging to the
same group identity, which is a crucial task for video surveillance. Existing methods only …
same group identity, which is a crucial task for video surveillance. Existing methods only …