Learning to Dispatch for Flexible Job Shop Scheduling Based on Deep Reinforcement Learning via Graph Gated Channel Transformation

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...

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Bibliographic Details
Main Authors: Dainlin HUANG, Hong Zhao, Lijun Zhang, Kangping Chen
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Flexible job-shop scheduling
deep reinforcement learning
graph gated channel transformation
global features
lightweight attention mechanism
Online Access:https://ieeexplore.ieee.org/document/10489964/

Internet

https://ieeexplore.ieee.org/document/10489964/

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