Sökresultat - "reinforcement learning"

A reinforcement learning-based approach for solving multi-agent job shop scheduling problem.

Dong, Zhuoran¹ (AUTHOR), Ren, Tao¹ (AUTHOR) chinarentao@163.com, Qi, Fang¹ (AUTHOR), Weng, Jiacheng¹ (AUTHOR), Bai, Danyu² (AUTHOR) mikebdy@163.com, Yang, Jie² (AUTHOR), Wu, Chin-Chia³ (AUTHOR)

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment.

Nievas, Nuria^1,2 (AUTHOR) nuria.nievas@eurecat.org, Pagès-Bernaus, Adela² (AUTHOR), Abio, Albert¹ (AUTHOR), Lange, Danillo¹ (AUTHOR), Garcia-Llamas, Eduard³ (AUTHOR), Grané, Marc³ (AUTHOR), Pujante, Jaume³ (AUTHOR), Echeverria, Lluís¹ (AUTHOR), Bonada, Francesc¹ (AUTHOR)

Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops.

Huang, Yuzhe¹ (AUTHOR), Fu, Gaocai¹ (AUTHOR) fgc1989@whut.edu.cn, Sheng, Buyun^1,2 (AUTHOR), Lu, Yingkang¹ (AUTHOR), Yu, Junpeng¹ (AUTHOR), Yin, Xiyan² (AUTHOR)

Improving efficiency of steel converter facility: a digital twin and reinforcement learning approach.

Kumar, Amit¹ (AUTHOR), Kumar, Ramesh¹ (AUTHOR) rameshkr8190@gmail.com

Multi-agent deep reinforcement learning for integrated production and maintenance optimisation in shared manufacturing.

Peng, Xiaoshuai¹ (AUTHOR), Wang, Shiyi¹ (AUTHOR), Wang, Kangzhou¹ (AUTHOR) kzhwang@lzu.edu.cn

Solving flexible job-shop problem considering skilled workers via multi-domain graph attention network.

Zhong, Ruirui^1,2 (AUTHOR), Feng, Yixiong^1,2 (AUTHOR) fyxtv@zju.edu.cn, Zhang, Ansi¹ (AUTHOR), Li, Chuanjiang¹ (AUTHOR), Jin, Kebing¹ (AUTHOR), Li, Zhiwu³ (AUTHOR), Tan, Jianrong² (AUTHOR)

Hierarchical multi-agent deep reinforcement learning for dynamic flexible job-shop scheduling with transportation.

Wang, Wenda¹ (AUTHOR), Zhang, Yi¹ (AUTHOR), Wang, Yong² (AUTHOR), Pan, Ge¹ (AUTHOR), Feng, Yiping¹ (AUTHOR) ypfeng@zju.edu.cn

Real-time sustainable cobotic disassembly planning using fuzzy reinforcement learning.

Amirnia, Ashkan¹ (AUTHOR) ashkan.amirnia@polymtl.ca, Keivanpour, Samira¹ (AUTHOR)

A joint work package sizing and scheduling problem considering resource constraints with a look-ahead heterogeneous reinforcement learning method.

Zhang, Nianmin¹ (AUTHOR), Li, Xiao¹ (AUTHOR) shell.x.li@hku.hk, Gao, Yao² (AUTHOR)

Robust seru production optimisation under uncertain worker processing times.

Wen, Muyang¹ (AUTHOR), Zhang, Yuli¹ (AUTHOR) zhangyuli@bit.edu.cn, Hu, Linyuan¹ (AUTHOR), Wang, Ting^1,2 (AUTHOR)

Dynamic scheduling for flexible job-shop with reconfigurable manufacturing cells considering dynamic job arrivals based on deep reinforcement learning.

Zheng, Liang¹ (AUTHOR), Chen, Xiaodi¹ (AUTHOR), Zhuang, Cunbo¹ (AUTHOR) zhuangdavid@bit.edu.cn, Liu, Jianhua¹ (AUTHOR), Zhang, Yongyang¹ (AUTHOR), Lai, Lijuan¹ (AUTHOR)

A dual-critic DQN architecture for lifter assignment in multi-floor semiconductor FAB.

Kim, Jiwon¹ (AUTHOR), Mo, Jeonghoon¹ (AUTHOR) j.mo@yonsei.ac.kr

Integrated scheduling of multi-objective lot-streaming hybrid flowshop with AGV based on deep reinforcement learning.

Tang, Hongtao¹ (AUTHOR) tanght@zjut.edu.cn, Huang, Jiawei¹ (AUTHOR), Ren, Chenhao¹ (AUTHOR), Shao, Yiping¹ (AUTHOR), Lu, Jiansha¹ (AUTHOR)

Real-time stochastic flexible flow shop scheduling in a credit factory with model-based reinforcement learning.

Chen, Liao^1,2 (AUTHOR), Liu, Hongjia^1,2 (AUTHOR), Jia, Ning^1,2 (AUTHOR) jia_ning@tju.edu.cn, Ren, Nianlu^1,2 (AUTHOR), Cui, Runbang³ (AUTHOR), Wei, Wei³ (AUTHOR)

Deep reinforcement learning approach for dynamic capacity planning in decentralised regenerative medicine supply chains.

Tseng, Chin-Yuan^1,2 (AUTHOR) ctseng40@gatech.edu, Li, Junxuan³ (AUTHOR), Lin, Li-Hsiang⁴ (AUTHOR), Wang, Kan² (AUTHOR), White III, Chelsea C.¹ (AUTHOR), Wang, Ben¹ (AUTHOR)

Towards Industry 5.0: digital twin-enhanced approach for dynamic supply chain rescheduling with real-time order arrival and acceptance.

Zhu, Xing¹ (AUTHOR), Liao, Baoyu² (AUTHOR) baoyu_liao@163.com, Shen, Yexing³ (AUTHOR), Kong, Min³ (AUTHOR)

The beer game bullwhip effect mitigation: a deep reinforcement learning approach.

Rozhkov, Maxim¹ (AUTHOR) maksim.rozhkov@hse.ru, Alyamovskaya, Nataliya¹ (AUTHOR), Zakhodiakin, Gleb¹ (AUTHOR)

Cost-effective dynamic sampling in high dimensional online monitoring with advantage actor-critic.

Li, Haoqian¹ (AUTHOR), Chen, Yudong¹ (AUTHOR), Liu, Kaibo¹ (AUTHOR) kliu8@wisc.edu

Minimising makespan in distributed assembly hybrid flowshop scheduling problems.

Ying, Kuo-Ching¹ (AUTHOR), Lin, Shih-Wei^2,3,4 (AUTHOR) swlin@mail.cgu.edu.tw, Chen, Kuan-Fu^3,5 (AUTHOR)

Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach.

Bussieweke, Fabian¹ (AUTHOR), Mula, Josefa¹ (AUTHOR) fmula@cigip.upv.es, Campuzano-Bolarin, Francisco² (AUTHOR)