International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 22
Опубликована: Июнь 4, 2025
Язык: Английский
International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 22
Опубликована: Июнь 4, 2025
Язык: Английский
Journal of Manufacturing Systems, Год журнала: 2024, Номер 77, С. 962 - 989
Опубликована: Ноя. 13, 2024
Язык: Английский
Процитировано
3Computers & Industrial Engineering, Год журнала: 2024, Номер unknown, С. 110768 - 110768
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
3Journal 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
0Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101885 - 101885
Опубликована: Фев. 21, 2025
Язык: Английский
Процитировано
0Neurocomputing, Год журнала: 2025, Номер unknown, С. 129760 - 129760
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111060 - 111060
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 150, С. 110598 - 110598
Опубликована: Апрель 4, 2025
Язык: Английский
Процитировано
0Computers & Operations Research, Год журнала: 2025, Номер unknown, С. 107087 - 107087
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 643 - 661
Опубликована: Апрель 14, 2025
Язык: Английский
Процитировано
0Science 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