Deep-Reinforcement-Learning-Based Production Scheduling in Industrial Internet of Things-Reference-Cited by-同舟云学术

Deep-Reinforcement-Learning-Based Production Scheduling in Industrial Internet of Things

Published:2023-11-15 Issue:22 Volume:10 Page:19725-19739
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Luo Zihui¹^ORCID,Jiang Chengling¹,Liu Liang¹^ORCID,Zheng Xiaolong¹^ORCID,Ma Huadong¹^ORCID,Dong Fang²^ORCID,Li Fucun³

Affiliation:

1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

2. School of Computer Science and Engineering, Southeast University, Nanjing, China

3. Jinheng Technology, Nanjing Iron and Steel United Company Ltd., Nanjing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

A3 Foresight Program of NSFC

Funds for Creative Research Groups of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10301752/10144304.pdf?arnumber=10144304

Reference46 articles.

1. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

2. Playing Atari with deep reinforcement learning;mnih;arXiv 1312 5602,2013

3. A Functional Heuristic Algorithm for the Flowshop Scheduling Problem

4. A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem

5. Ant colony optimization

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