Reinforcement Learning-based Job Shop Scheduling for Remanufacturing Production-Reference-Cited by-同舟云学术

Reinforcement Learning-based Job Shop Scheduling for Remanufacturing Production

Published:2022-12-07 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
language:
Short-container-title:

Author:

Bai Yue¹,Lv Yaqiong¹

Affiliation:

1. Wuhan University of Technology,School of Transportation and Logistics Engineering,Wuhan,China

Funder

Ministry of Education

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9989527/9989519/09989643.pdf?arnumber=9989643

Reference17 articles.

1. Dynamic Jobshop Scheduling Algorithm Based on Deep Q Network

2. Deep Q-Network Model for Dynamic Job Shop Scheduling Problem Based on Discrete Event Simulation

3. Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

4. Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing;yang;International Journal of Production Research,2021

5. Autonomous decision-making method of transportation process for flexible job shop scheduling problem based on reinforcement learning

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-time scheduling for two-stage assembly flowshop with dynamic job arrivals by deep reinforcement learning;Advanced Engineering Informatics;2024-10

2. Energy Estimation and Production Scheduling in Job Shop Using Machine Learning;IEEE Access;2024

3. Smart mobile robot fleet management based on hierarchical multi-agent deep Q network towards intelligent manufacturing;Engineering Applications of Artificial Intelligence;2023-09