Learning to Dispatch for Flexible Job Shop Scheduling Based on Deep Reinforcement Learning via Graph Gated Channel Transformation
In addressing the Flexible Job Shop Scheduling Problem (FJSP), deep reinforcement learning eliminates the need for mathematical modeling of the problem, requiring only interaction with the real environment to learn effective strategies. Using disjunctive graphs as the state representation has proven...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10489964/ |