Solving flexible job-shop problem considering skilled workers via multi-domain graph attention network DOI
Ruirui Zhong, Yixiong Feng, Ansi Zhang

и другие.

International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Июнь 4, 2025

Язык: Английский

Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review DOI Creative Commons
Chao Zhang, Max Juraschek, Christoph Herrmann

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 77, С. 962 - 989

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

3
A deep reinforcement learning method based on a multiexpert graph neural network for flexible job shop scheduling DOI
Dailin Huang, Hong Zhao,

W. H. Tian

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер unknown, С. 110768 - 110768

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

3
Study on the Multi-Equipment Integrated Scheduling Problem of a U-Shaped Automated Container Terminal Based on Graph Neural Network and Deep Reinforcement Learning DOI Creative Commons
Qinglei Zhang, Yi Zhu,

Jiyun Qin

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(2), С. 197 - 197

Опубликована: Янв. 22, 2025

Intelligent Guided Vehicles (IGVs) in U-shaped automated container terminals (ACTs) have longer travel paths than those conventional vertical layout ACTs, and their interactions with double trolley quay cranes (DTQCs) cantilever rail (DCRCs) are more frequent complex, so the scheduling strategy of a traditional ACT cannot easily be applied to ACT. With aim minimizing maximum task completion times within ACT, this study investigates integrated problem DTQCs, IGVs DCRCs under hybrid “loading unloading” mode, expresses as Markovian decision-making process, establishes disjunctive graph model. A deep reinforcement learning algorithm based on neural network combined proximal policy optimization is proposed. To verify superiority proposed models algorithms, instances different scales were stochastically generated compare method several heuristic algorithms. This also analyses idle time equipment two loading unloading modes, results show that mode can enhance operational effectiveness.

Язык: Английский

Процитировано

0
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

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101885 - 101885

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

0
TDCR: Transformer based decision conflict resolution model for collaborative scheduling DOI

Xian‐Zhang Hu,

Jian An,

Xiaolin Gui

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129760 - 129760

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Harnessing heterogeneous graph neural networks for Dynamic Job-Shop Scheduling Problem solutions DOI
Chien‐Liang Liu, P.S. Weng, Chun-Jan Tseng

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111060 - 111060

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Solving quay wall allocation problems based on deep reinforcement learning DOI

Young-in Cho,

Seung-Heon Oh,

J U Choi

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110598 - 110598

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0
Deep reinforcement learning for solving efficient and energy-saving flexible job shop scheduling problem with multi-AGV DOI
Weiyao Cheng, Chaoyong Zhang, Leilei Meng

и другие.

Computers & Operations Research, Год журнала: 2025, Номер unknown, С. 107087 - 107087

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem DOI
Youshan Liu, Jiaxin Fan, Weiming Shen

и другие.

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 643 - 661

Опубликована: Апрель 14, 2025

Язык: Английский

Процитировано

0
Dynamic job shop scheduling under multiple order disturbances using deep reinforcement learning DOI Creative Commons

Sun Zhi-yuan,

Wenmin Han,

Longlong Gao

и другие.

Science Progress, Год журнала: 2025, Номер 108(2)

Опубликована: Апрель 1, 2025

Dynamic job shop scheduling problems with multiple order disturbances present significant challenges in manufacturing systems. This paper proposes a novel approach using Independent Proximal Policy Optimization (IPPO), multiagent deep reinforcement learning algorithm, to address these challenges. We introduce five-channel two-dimensional image represent system states and design reward function that minimizes both total tardiness makespan. Experimental results across 72 diverse production scenarios demonstrate our IPPO-based outperforms traditional algorithms dispatching rules most cases. The proposed method shows strong optimization exploration capabilities, offering promising solution for complex, multiobjective dynamic environments.

Язык: Английский

Процитировано

0