An End-to-end Hierarchical Reinforcement Learning Framework for Large-scale Dynamic Flexible Job-shop Scheduling Problem-Reference-Cited by-同舟云学术

An End-to-end Hierarchical Reinforcement Learning Framework for Large-scale Dynamic Flexible Job-shop Scheduling Problem

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Lei Kun¹,Guo Peng¹,Wang Yi²,Xiong Jianyu¹,Zhao Wenchao¹

Affiliation:

1. School of Mechanical Engineering, Southwest Jiaotong University,Chengdu,China,610031

2. Auburn University at Montgomery,Department of Mathematics,Montgomery,AL,USA,36124-4023

Funder

National Key Research and Development Plan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892005.pdf?arnumber=9892005

Reference21 articles.

1. Two-stage teaching-learning-based optimization method for flexible job-shop scheduling under machine breakdown

2. Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm

3. Learning to dispatch for job shop scheduling via deep reinforcement learning;zhang;Advances in neural information processing systems,2020

4. Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

5. A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems;ma;Advances in neural information processing systems,2021

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