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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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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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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Real-time scheduling for two-stage assembly flowshop with dynamic job arrivals by deep reinforcement learning

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102632 - 102632

Published: June 19, 2024

Language: Английский

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Dynamic flexible job-shop scheduling by multi-agent reinforcement learning with reward-shaping

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102872 - 102872

Published: Oct. 1, 2024

Language: Английский

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A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 91, P. 101764 - 101764

Published: Nov. 9, 2024

Language: Английский

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A new dispatching mechanism for parallel-machine scheduling with different efficiencies and sequence-dependent setup times

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: 10, P. 100432 - 100432

Published: Feb. 22, 2024

The Apparent Tardiness Cost (ATC) dispatching rule was initially developed to minimize tardiness in single-machine scheduling problems. ATC extensions have been frequently applied other production settings, relying heavily on blocking idle machine capacity with a outlook; this approach may not result the best outcomes, considering that machines different efficiencies. This study develops new for parallel-machine scheduling, efficiencies, ready times, and sequence-dependent setup times total weighted tardiness. proposed method reduces time interference factor of denominator item uses more effective methods selecting processing jobs. grid is used evaluate against state-of-the-art. experimental results confirm superior regardless type parallel machines, problem scale, operational parameters. It also shown rules can be improved by applying approach. could incorporated into soft computing techniques efficient scheduling.

Language: Английский

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