A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem DOI
Jiawei Wu, Yong Liu

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 140, P. 109688 - 109688

Published: Nov. 27, 2024

Language: Английский

Reinforcement learning for distributed hybrid flowshop scheduling problem with variable task splitting towards mass personalized manufacturing DOI
Xin Chen, Yibing Li, Kaipu Wang

et al.

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 76, P. 188 - 206

Published: Aug. 3, 2024

Language: Английский

Citations

8
Deep Reinforcement Learning and Discrete Simulation-Based Digital Twin for Cyber–Physical Production Systems DOI Creative Commons
Damian Krenczyk

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 5208 - 5208

Published: June 14, 2024

One of the goals developing and implementing Industry 4.0 solutions is to significantly increase level flexibility autonomy production systems. It intended provide possibility self-reconfiguration systems create more efficient adaptive manufacturing processes. Achieving such requires comprehensive integration digital technologies with real processes towards creation so-called Cyber–Physical Production Systems (CPPSs). Their architecture based on physical cybernetic elements, a twin as central element “cyber” layer. However, for responses obtained from cyber layer, allow quick response changes in environment system, its virtual counterpart must be supplemented advanced analytical modules. This paper proposes method creating system discrete simulation models integrated deep reinforcement learning (DRL) techniques CPPSs. Here, which agent communicates find strategy allocating resources. Asynchronous Advantage Actor–Critic Proximal Policy Optimization algorithms were selected this research.

Language: Английский

Citations

5
Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources DOI
Xinxin Zhou, Feimeng Wang, Bin Wu

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101885 - 101885

Published: Feb. 21, 2025

Language: Английский

Citations

0
Dynamic flexible flow shop scheduling via cross-attention networks and multi-agent reinforcement learning DOI

Jinlong Zheng,

Yixin Zhao,

Yinya Li

et al.

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 80, P. 395 - 411

Published: March 27, 2025

Language: Английский

Citations

0
Terminal normalization in genetic programming for dynamic flexible job shop scheduling DOI
Binzi Xu, Xinyu Cao, Shuzhu Zhang

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 96, P. 101970 - 101970

Published: May 15, 2025

Language: Английский

Citations

0
Reinforcement Learning–Based Multiobjective and Multiconstraint Production Scheduling for Precast Concrete DOI

Leting Zu,

Wenzhu Liao

Journal of Construction Engineering and Management, Journal Year: 2025, Volume and Issue: 151(8)

Published: May 19, 2025

Language: Английский

Citations

0
Dynamic production scheduling and maintenance planning under opportunistic grouping DOI

Nada Ouahabi,

Ahmed Chebak,

Oulaïd Kamach

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110646 - 110646

Published: Oct. 1, 2024

Language: Английский

Citations

3
A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem DOI
Jiawei Wu, Yong Liu

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 140, P. 109688 - 109688

Published: Nov. 27, 2024

Language: Английский

Citations

2