Multi-Constraint Flexible Job Scheduling Algorithm Based on DDQN: A Deep Reinforcement Learning Algorithm for Solving Practical Job Scheduling Problems-Reference-Cited by-同舟云学术

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Multi-Constraint Flexible Job Scheduling Algorithm Based on DDQN: A Deep Reinforcement Learning Algorithm for Solving Practical Job Scheduling Problems

Published:2023-11-17 Issue: Volume:43 Page:248-256
ISSN:
Container-title:Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering
language:
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Author:

Liu Lin¹^ORCID,Liang Zijing¹^ORCID,Hong Yifan¹^ORCID,Liu Ke¹^ORCID,Wang Hui¹^ORCID,Shang Penghui¹^ORCID

Affiliation:

1. Hangzhou Zhiyuan Research Institute Co., Ltd., China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652628.3652670

Reference22 articles.

1. Current Situation and Future Paths of Green Manufacturing and Intelligent Manufacturing Development in China [J];Li Jinhua;Economic and Management Research,2022

2. Huang Xuewen, Zhang Xiaotong, Sun Rong, Flexible Scheduling Algorithm for Process Paths Based on Genetic Algorithm [J]. Operations Research and Management Science, 2018, 27(06): 184-193.

3. A new Lagrangian relaxation algorithm for hybrid flowshop scheduling to minimize total weighted completion time

4. A branch and bound algorithm for hybrid flow shop scheduling problem with setup time and assembly operations

5. Branch-and-bound and PSO algorithms for no-wait job shop scheduling

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