Machine-fixture-pallet resources constrained flexible job shop scheduling considering loading and unloading times under pallet automation system

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 143 - 158

Опубликована: Фев. 6, 2024

Язык: Английский

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Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning

Journal of Manufacturing Systems, Год журнала: 2025, Номер 79, С. 179 - 198

Опубликована: Янв. 24, 2025

Язык: Английский

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Dynamic job-shop scheduling using graph reinforcement learning with auxiliary strategy

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 1 - 18

Опубликована: Янв. 20, 2024

Язык: Английский

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Multi-agent deep reinforcement learning-based approach for dynamic flexible assembly job shop scheduling with uncertain processing and transport times

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126441 - 126441

Опубликована: Янв. 1, 2025

Язык: Английский

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A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion

Applied Soft Computing, Год журнала: 2024, Номер 164, С. 111937 - 111937

Опубликована: Июль 6, 2024

Язык: Английский

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Deep reinforcement learning for dynamic distributed job shop scheduling problem with transfers

Expert Systems with Applications, Год журнала: 2024, Номер 251, С. 123970 - 123970

Опубликована: Апрель 17, 2024

Язык: Английский

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Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

Computers & Industrial Engineering, Год журнала: 2024, Номер 189, С. 109995 - 109995

Опубликована: Фев. 27, 2024

Язык: Английский

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A discrete event simulator to implement deep reinforcement learning for the dynamic flexible job shop scheduling problem

Simulation Modelling Practice and Theory, Год журнала: 2024, Номер 134, С. 102948 - 102948

Опубликована: Апрель 21, 2024

The job shop scheduling problem, which involves the routing and sequencing of jobs in a context, is relevant subject industrial engineering. Approaches based on Deep Reinforcement Learning (DRL) are very promising for dealing with variability real working conditions due to dynamic events such as arrival new machine failures. Discrete Event Simulation (DES) essential training testing DRL approaches, interaction an intelligent agent production system. Nonetheless, there numerous papers literature techniques, developed solve Dynamic Flexible Job Shop Problem (DFJSP), have been implemented evaluated absence simulation environment. In paper, limitations these techniques highlighted, numerical experiment that demonstrates their ineffectiveness presented. Furthermore, order provide scientific community tool designed be used conjunction agent-based discrete event simulator also

Язык: Английский

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Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101605 - 101605

Опубликована: Май 28, 2024

Язык: Английский

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Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 91, С. 102834 - 102834

Опубликована: Июль 18, 2024

Язык: Английский

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