Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing-Reference-Cited by-同舟云学术

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Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing

Published:2021-07-19 Issue: Volume: Page:1-18
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Yang Shengluo¹²³^ORCID,Xu Zhigang¹²

Affiliation:

1. Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, People’s Republic of China

2. Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang, People’s Republic of China

3. University of Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

the Natural Science Foundation of Liaoning

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1943037

Reference47 articles.

1. Reconfigurable manufacturing systems: Literature review and research trend

2. Concurrent optimisation of modular product and Reconfigurable Manufacturing System configuration: a customer-oriented offer for mass customisation

3. Bi-objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II

4. Bi-objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II

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1. Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation;Robotics and Computer-Integrated Manufacturing;2025-02

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

3. Smart scheduling for next generation manufacturing systems: a systematic literature review;Journal of Intelligent Manufacturing;2024-09-09

4. Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling;Engineering Applications of Artificial Intelligence;2024-09

5. Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies;IET Collaborative Intelligent Manufacturing;2024-09

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