Learning to select operators in meta-heuristics: an integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
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Publication:2079447
DOI10.1016/j.ejor.2022.03.054OpenAlexW4226164141MaRDI QIDQ2079447
Maryam Karimi-Mamaghan, Bastien Pasdeloup, Patrick Meyer, Mehrdad Mohammadi
Publication date: 29 September 2022
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2022.03.054
combinatorial optimizationreinforcement learningQ-learning algorithmiterated greedy meta-heuristicpermutation flowshop scheduling problem
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Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II ⋮ Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems
Uses Software
Cites Work
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