Mathematical model and knowledge-based iterated greedy algorithm for distributed assembly hybrid flow shop scheduling problem with dual-resource constraints

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 239, P. 122434 - 122434

Published: Nov. 4, 2023

Language: Английский

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A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 196, P. 110484 - 110484

Published: Aug. 18, 2024

Language: Английский

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MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 85, P. 101479 - 101479

Published: Jan. 10, 2024

Language: Английский

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HGNP: A PCA-based heterogeneous graph neural network for a family distributed flexible job shop

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110855 - 110855

Published: Jan. 1, 2025

Language: Английский

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Ensemble meta-heuristics and Q-learning for solving unmanned surface vessels scheduling problems

Swarm and Evolutionary Computation, Journal Year: 2023, Volume and Issue: 82, P. 101358 - 101358

Published: July 7, 2023

Language: Английский

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A tri-individual iterated greedy algorithm for the distributed hybrid flow shop with blocking

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121667 - 121667

Published: Sept. 20, 2023

Language: Английский

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Hyper-heuristics: A survey and taxonomy

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 187, P. 109815 - 109815

Published: Dec. 4, 2023

Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-heuristics to solve challenging optimization problems. They differ from traditional methods, which primarily employ space-based strategies. Due the remarkable performance of hyper-heuristics in multi-objective machine learning-based optimization, there has been an increasing interest this field. With a fresh perspective, our work extends current taxonomy presents overview most significant hyper-heuristic studies last two decades. Four categories under we analyze selection (including learning techniques), low-level heuristics, target problems, parallel hyper-heuristics. Future research prospects, trends, prospective fields study also explored.

Language: Английский

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A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107818 - 107818

Published: Jan. 9, 2024

Language: Английский

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Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 191, P. 110155 - 110155

Published: April 15, 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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