Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly-Reference-Cited by-同舟云学术

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Integrating multi-agent reinforcement learning and 3D A* search for facility layout problem considering connector-assembly

Published:2023-09-27 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Zhang Qiaoyu^ORCID,Lin Yan

Funder

Foundation of State Key Laboratory of Structural Analysis for Industrial Equipment

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02209-x.pdf

Reference66 articles.

1. Aiello, G., La Scalia, G., & Enea, M. (2013). A non-dominated ranking multi objective genetic algorithm and electre method for unequal area facility layout problems. Expert Systems with Applications, 40(12), 4812–4819. https://doi.org/10.1016/j.eswa.2013.02.026

2. Ajiwaskita, F., Gunawan, G., & Yanuar, Y. (2020). Pipe-routing optimization using system engineering methodology in ship engine room. AIP Conference Proceedings, 2227, 1–7. https://doi.org/10.1063/5.0001001

3. Angelova, M., & Pencheva, T. (2011). Tuning genetic algorithm parameters to improve convergence time. International Journal of Chemical Engineering. https://doi.org/10.1155/2011/646917

4. Anjos, M. F., & Vieira, M. V. C. (2017). Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions. European Journal of Operational Research, 261, 1–16. https://doi.org/10.1016/j.ejor.2017.01.049

5. Awheda, M., & Schwartz, H. M. (2013). Exponential moving average Q-learning algorithm. In Proceedings of the IEEE symposium series on computational intelligence, Singapore, April 15–19. https://doi.org/10.1109/ADPRL.2013.6614986

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