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

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 73, P. 143 - 158

Published: Feb. 6, 2024

Language: Английский

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

Journal of Manufacturing Systems, Journal Year: 2025, Volume and Issue: 79, P. 179 - 198

Published: Jan. 24, 2025

Language: Английский

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

Journal of Manufacturing Systems, Journal Year: 2024, Volume and Issue: 73, P. 1 - 18

Published: Jan. 20, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 164, P. 111937 - 111937

Published: July 6, 2024

Language: Английский

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

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 189, P. 109995 - 109995

Published: Feb. 27, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 91, P. 102834 - 102834

Published: July 18, 2024

Language: Английский

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

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 251, P. 123970 - 123970

Published: April 17, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 134, P. 102948 - 102948

Published: April 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

Language: 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, Journal Year: 2024, Volume and Issue: 88, P. 101605 - 101605

Published: May 28, 2024

Language: Английский

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Graph neural networks for job shop scheduling problems: A survey

Computers & Operations Research, Journal Year: 2024, Volume and Issue: unknown, P. 106914 - 106914

Published: Nov. 1, 2024

Language: Английский

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