Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 86, P. 101517 - 101517

Published: Feb. 27, 2024

Language: Английский

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A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101467 - 101467

Published: Jan. 1, 2024

Language: Английский

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Evolutionary algorithm incorporating reinforcement learning for energy-conscious flexible job-shop scheduling problem with transportation and setup times

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 107974 - 107974

Published: Feb. 13, 2024

Language: Английский

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Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 40, P. 100620 - 100620

Published: May 3, 2024

Language: Английский

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A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101497 - 101497

Published: Feb. 2, 2024

Language: Английский

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

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Scheduling Multiobjective Dynamic Surgery Problems via Q-Learning-Based Meta-Heuristics

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2024, Volume and Issue: 54(6), P. 3321 - 3333

Published: Feb. 21, 2024

This work addresses multiobjective dynamic surgery scheduling problems with considering uncertain setup time and processing time. When dealing them, researchers have to consider rescheduling due the arrivals of urgent patients. The goals are minimize fuzzy total medical cost, maximum completion time, maximize average patient satisfaction. First, we develop a mathematical model for describing addressed problems. is expressed by triangular numbers. Then, four meta-heuristics improved, eight variants developed, including artificial bee colony, genetic algorithm, teaching-learning-base optimization, imperialist competitive algorithm. For improving initial solutions' quality, two initialization strategies developed. Six local search proposed fine exploitation $Q$ -learning algorithm used choose suitable among them in iterative process meta-heuristics. states actions defined according characteristic Finally, algorithms tested 57 instances different scales. analysis discussions verify that improved colony most one all compared algorithms.

Language: Английский

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A self-adaptive co-evolutionary algorithm for multi-objective flexible job-shop rescheduling problem with multi-phase processing speed selection, condition-based preventive maintenance and dynamic repairman assignment

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 89, P. 101643 - 101643

Published: July 1, 2024

Language: Английский

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A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 91, P. 101760 - 101760

Published: Nov. 4, 2024

Language: Английский

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A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop

Computers & Operations Research, Journal Year: 2024, Volume and Issue: unknown, P. 106919 - 106919

Published: Nov. 1, 2024

Language: Английский

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