Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs-Reference-Cited by-同舟云学术

Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs

Published:2023-10-11 Issue:1-2 Volume:62 Page:404-420
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Pan Ruilin¹²,Wang Qiong¹,Cao Jianhua¹²,Zhou Chunliu¹²

Affiliation:

1. School of Management Science and Engineering, Anhui University of Technology, Ma'anshan, People’s Republic of China

2. Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma'anshan, People’s Republic of China

Funder

National Natural Science Foundation of China

University Natural Science Research Project of Anhui Province

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2267693

Reference46 articles.

1. Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem

2. A New Scalarization Technique to Approximate Pareto Fronts of Problems with Disconnected Feasible Sets

3. A novel shuffled frog-leaping algorithm with reinforcement learning for distributed assembly hybrid flow shop scheduling

4. An efficient greedy insertion heuristic for energy-conscious single machine scheduling problem under time-of-use electricity tariffs

5. A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing

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