HGNP: A PCA-based heterogeneous graph neural network for a family distributed flexible job shop

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110855 - 110855

Опубликована: Янв. 1, 2025

Язык: Английский

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Dynamic scheduling for flexible job shop with insufficient transportation resources via graph neural network and deep reinforcement learning

Computers & Industrial Engineering, Год журнала: 2023, Номер 186, С. 109718 - 109718

Опубликована: Окт. 31, 2023

Язык: Английский

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Год журнала: 2024, Номер 120, С. 109780 - 109780

Опубликована: Окт. 18, 2024

Язык: Английский

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A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108487 - 108487

Опубликована: Апрель 26, 2024

Язык: Английский

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Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 90, С. 101660 - 101660

Опубликована: Июль 18, 2024

Язык: Английский

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

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102646 - 102646

Опубликована: Июнь 26, 2024

Язык: Английский

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Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110856 - 110856

Опубликована: Янв. 1, 2025

Язык: Английский

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An end-to-end decentralised scheduling framework based on deep reinforcement learning for dynamic distributed heterogeneous flowshop scheduling

International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Янв. 23, 2025

Язык: Английский

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Exploring multi-agent reinforcement learning for unrelated parallel machine scheduling

The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)

Опубликована: Фев. 19, 2025

Язык: Английский

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An efficient and adaptive design of reinforcement learning environment to solve job shop scheduling problem with soft actor-critic algorithm

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 16

Опубликована: Март 30, 2024

Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied solve problems like job shop flow shop. But most studies focus on using latest algorithms while ignoring that environment plays an important role agent learning. this paper, we design effective, robust size-agnostic for scheduling. The proposed uses centralised training decentralised execution (CTDE) implement a multi-agent architecture. Together with observation space design, environmental information irrelevant current decision eliminated as much possible. action enlarges agents, which performs better than traditional way. Finally, Soft Actor-Critic (SAC) algorithm adapted learning within environment. By comparing rules, other reinforcement algorithms, relevant literature, superiority results obtained study demonstrated.

Язык: Английский

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