Name
Title | ||
|---|---|---|
A graph neural networks-based deep Q-learning approach for job shop scheduling problems in traffic management |
Abstract | ||
|---|---|---|
•An end-to-end framework to solve JSSPs by using graph neural networks and dueling double deep Q network.•This single policy model is suitable for solving instances that have similar size and is trained only by observing reward signals and following feasible rules.•The trained model behaves like a constructive heuristic algorithm that incrementally constructs a solution, and each action is determined by the output of a Graph Neural Network (GNN) which captures the current state of the partial solution. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.06.017 | Information Sciences |
Keywords | DocType | Volume |
Traffic JSSP,Graph neural network,Deep Q-learning | Journal | 607 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyi Liu | 1 | 0 | 0.34 |
| Yuan Wang | 2 | 0 | 0.34 |
| Xingxing Liang | 3 | 0 | 1.01 |
| Yang Ma | 4 | 0 | 0.34 |
| Yanghe Feng | 5 | 0 | 0.34 |
| Guangquan Cheng | 6 | 0 | 0.34 |
| Zhong Liu | 7 | 148 | 26.70 |