[HTML][HTML] Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review

C Zhang, M Juraschek, C Herrmann - Journal of Manufacturing Systems, 2024 - Elsevier
Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time
adjustments to production schedules, thereby enhancing system resilience and promoting …

Collaborative dynamic scheduling in a self-organizing manufacturing system using multi-agent reinforcement learning

Y Gui, Z Zhang, D Tang, H Zhu, Y Zhang - Advanced Engineering …, 2024 - Elsevier
Personalized product demands have made the production mode of many varieties and small
batches mainstream. Self-organizing manufacturing systems represented by multi-agent …

A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

JP Huang, L Gao, XY Li, CJ Zhang - Computers & Industrial Engineering, 2023 - Elsevier
Distributed manufacturing can reduce the production cost through the cooperation among
factories, and it has been an important trend in the industrial field. For the enterprises with …

A review of reinforcement learning based hyper-heuristics

C Li, X Wei, J Wang, S Wang, S Zhang - PeerJ Computer Science, 2024 - peerj.com
The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of
optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the …

[HTML][HTML] A logic Petri net model for dynamic multi-agent game decision-making

H Byeon, C Thingom, I Keshta, M Soni… - Decision Analytics …, 2023 - Elsevier
This study proposes a logical Petri net model to leverage the modeling advantages of Petri
nets in handling batch processing and uncertainty in value passing and to integrate relevant …

A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem

Y Di, L Deng, L Zhang - Swarm and Evolutionary Computation, 2024 - Elsevier
As the increasing level of implementation of artificial intelligence technology in solving
complex engineering optimization problems, various learning mechanisms, including deep …

[HTML][HTML] Study on the application of single-agent and multi-agent reinforcement learning to dynamic scheduling in manufacturing environments with growing …

D Heik, F Bahrpeyma, D Reichelt - Journal of manufacturing systems, 2024 - Elsevier
Abstract Industry 4.0, smart manufacturing and smart products have recently attracted
substantial attention and are becoming increasingly prevalent in manufacturing systems. As …

Deep reinforcement learning driven trajectory-based meta-heuristic for distributed heterogeneous flexible job shop scheduling problem

Q Zhang, W Shao, Z Shao, D Pi, J Gao - Swarm and Evolutionary …, 2024 - Elsevier
As the production environment evolves, distributed manufacturing exhibits heterogeneous
characteristics, including diverse machines, workers, and production processes. This paper …

Dynamic job-shop scheduling using graph reinforcement learning with auxiliary strategy

Z Liu, H Mao, G Sa, H Liu, J Tan - Journal of Manufacturing Systems, 2024 - Elsevier
The unpredictable variety of dynamic events in manufacturing systems poses a great
challenge for tackling the job-shop scheduling problem (JSP), while most prior arts fail to …

Learning to schedule dynamic distributed reconfigurable workshops using expected deep Q-network

S Yang, J Wang, Z Xu - Advanced Engineering Informatics, 2024 - Elsevier
Distributed manufacturing has become common in the globalized production environment.
Facing the tendency of mass customization, distributed workshops are required to have the …