An effective reformative memetic algorithm for distributed flexible job-shop scheduling problem with order cancellation

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121205 - 121205

Published: Aug. 17, 2023

Language: Английский

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An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121756 - 121756

Published: Sept. 27, 2023

Language: 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, Journal Year: 2024, Volume and Issue: 133, P. 108487 - 108487

Published: April 26, 2024

Language: Английский

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An adaptive multi-objective multi-task scheduling method by hierarchical deep reinforcement learning

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 154, P. 111342 - 111342

Published: Feb. 3, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 90, P. 101660 - 101660

Published: July 18, 2024

Language: Английский

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Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning

Journal of Intelligent Manufacturing, Journal Year: 2023, Volume and Issue: 35(6), P. 2593 - 2610

Published: June 24, 2023

Language: Английский

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A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 185, P. 109650 - 109650

Published: Oct. 4, 2023

Language: Английский

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Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Sustainability, Journal Year: 2024, Volume and Issue: 16(8), P. 3234 - 3234

Published: April 12, 2024

In response to the challenges of dynamic adaptability, real-time interactivity, and optimization posed by application existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks complete task job shop scheduling. A distributed multi-agent architecture (DMASA) is constructed maximize global rewards, modeling intelligent manufacturing problem as sequential decision represented graphs Graph Embedding–Heterogeneous Neural Network (GE-HetGNN) encode state nodes map them optimal strategy, including machine matching process selection strategies. Finally, an actor–critic architecture-based proximal policy algorithm employed train network optimize decision-making process. Experimental results demonstrate that proposed framework exhibits generalizability, outperforms commonly used rules RL-based methods on benchmarks, shows better stability than single-agent architectures, breaks through instance-size constraint, making it suitable for large-scale problems. We verified feasibility our method specific experimental environment. The research can achieve formal mapping with physical processing workshops, which aligns more closely real-world green issues makes easier subsequent researchers integrate actual environments.

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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A multi-agent double Deep-Q-network based on state machine and event stream for flexible job shop scheduling problem

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 58, P. 102230 - 102230

Published: Oct. 1, 2023

Language: Английский

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