A multi-agent reinforcement learning based scheduling strategy for flexible job shops under machine breakdowns

L Lv, J Fan, C Zhang, W Shen - Robotics and Computer-Integrated …, 2025 - Elsevier
In a highly disrupted workshop environment, machine failures may occur frequently,
requiring real-time schedule repair strategies. This paper proposes a type-aware multi-agent …

Fusion q-learning algorithm for open shop scheduling problem with AGVs

X Wen, H Zhang, H Li, H Wang, W Ming, Y Zhang… - Mathematics, 2024 - mdpi.com
In accordance with the actual production circumstances of enterprises, a scheduling
problem model is designed for open-shop environments, considering AGV transport time. A …

Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning

L Zhang, Y Yan, Y Hu - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Reinforcement learning-based methods have addressed production scheduling problems
with flexible processing constraints. However, delayed rewards arise due to the dynamic …

End-to-end Multi-Target Flexible Job Shop Scheduling With Deep Reinforcement Learning

R Wang, Y Jing, C Gu, S He… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Modeling and solving the Flexible Job Shop Scheduling Problem (FJSP) is critical for
modern manufacturing. However, existing works primarily focus on the time-related …

[HTML][HTML] An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

Y Lu, Y Yuan, A Sitahong, Y Chao, Y Wang - Machines, 2024 - mdpi.com
This paper addresses the green permutation flow shop scheduling problem (GPFSP) with
energy consumption consideration, aiming to minimize the maximum completion time and …

MILP Models for Flexible Job Shop Scheduling with Spatial Constraints and Sequence Flexibility *

Y Han, S Peng, Z Shen, Z Tao, Y Lv… - 2024 IEEE 20th …, 2024 - ieeexplore.ieee.org
Within the evolving landscape of Industry 4.0, the significance of flexible job shop
scheduling problems is increasing. This study addresses a novel category of NP-hard …