Terminal normalization in genetic programming for dynamic flexible job shop scheduling

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 96, С. 101970 - 101970

Опубликована: Май 15, 2025

Язык: Английский

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Knowledge-driven inverse diffusion prediction algorithm for flexible job shop scheduling problem considering transportation resources and multiple breakdowns

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 96, С. 101979 - 101979

Опубликована: Май 22, 2025

Язык: Английский

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DQL-assisted competitive evolutionary algorithm for energy-aware robust flexible job shop scheduling under unexpected disruptions

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 91, С. 101750 - 101750

Опубликована: Окт. 25, 2024

Язык: Английский

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A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 140, С. 109688 - 109688

Опубликована: Ноя. 27, 2024

Язык: Английский

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Cooperative multi-agent reinforcement learning for multi-area integrated scheduling in wafer fabs

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Окт. 23, 2024

The existing scheduling methods of wafer fabs focus on single area, achieving local optimisation while failing to realise global due neglecting the coordination multi-area. Therefore, it is necessary consider complex opposing relationships between multi-area caused by constraints such as batch processing, re-entrance, and multiple residency times within areas conduct integrated shorten production cycle time. For this issue, paper proposes a cooperative multi-agent reinforcement learning for scheduling. Aiming at dynamic batching considering arrival lots in multi-area, algorithm presented learn optimal policy firstly. Subsequently, framework raised achieve Furthermore, an adaptive exploration strategy constructed enhance capability solution space time re-entrant property. Moreover, share enhanced Double DQN employed improve generalisation adaptability multi-agent. Finally, experiments demonstrate that proposed method has better comprehensive performance compared previous area-separated methods.

Язык: Английский

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A two-level evolutionary algorithm for dynamic scheduling in flexible job shop environment

Evolutionary Intelligence, Год журнала: 2024, Номер 17(5-6), С. 4133 - 4153

Опубликована: Авг. 23, 2024

Язык: Английский

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