Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems-Reference-Cited by-同舟云学术

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Improved Q ‐learning algorithm for solving permutation flow shop scheduling problems

Published:2021-09-21 Issue: Volume: Page:
ISSN:2516-8398
Container-title:IET Collaborative Intelligent Manufacturing
language:en
Short-container-title:IET collob. intell. manuf.

Author:

He Zimiao¹,Wang Kunlan²,Li Hanxiao¹,Song Hong³,Lin Zhongjie³,Gao Kaizhou¹³^ORCID,Sadollah Ali⁴

Affiliation:

1. School of Computer Liaocheng University Liaocheng China

2. Integrate Media Center of Zhu Cheng Weifang China

3. Institute of Systems Engineering, Macau University of Science and Technology Macau China

4. Department of Mechanical Engineering University of Science and Culture Tehran Iran

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/cim2.12042

Reference42 articles.

1. A survey on meta‐heuristics for solving disassembly line balancing, planning and scheduling problems in remanufacturing;Gao K.Z.;Swarm Evol. Comput.,2020

2. Optimal two- and three-stage production schedules with setup times included

3. A branch‐and‐bound algorithm for the exact solution of the three‐machine scheduling problem;Lomnicki Z.A.;J. Oper. Res. Soc.,1965

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

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1. The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem;Expert Systems with Applications;2024-12

2. A path relinking with tabu search algorithm for solving hybrid flow shop scheduling problem considering multiple critical paths;Computers & Operations Research;2024-10

3. Makine Çizelgeleme Problemlerinin Çözümünde Pekiştirmeli Öğrenme Etkisinin Analizi;ALKÜ Fen Bilimleri Dergisi;2024-08-30

4. Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey;WIREs Data Mining and Knowledge Discovery;2024-08-18

5. Development of a Less Dissipative Interface Variable Reconstruction to Solve the Euler Equations by Q Learning Method;Communications in Computational Physics;2024-06

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