Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem-Reference-Cited by-同舟云学术

Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem

Published:2023-02 Issue:3 Volume:304 Page:1296-1330
ISSN:0377-2217
Container-title:European Journal of Operational Research
language:en
Short-container-title:European Journal of Operational Research

Author:

Karimi-Mamaghan Maryam^ORCID,Mohammadi Mehrdad^ORCID,Pasdeloup Bastien^ORCID,Meyer Patrick

Publisher

Elsevier BV

Subject

Information Systems and Management,Management Science and Operations Research,Modeling and Simulation,General Computer Science,Industrial and Manufacturing Engineering

Reference84 articles.

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3. Population-based tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration;Arık;Soft computing,2020

4. Fast heuristics for minimizing the makespan in non-permutation flow shops;Benavides;Computers & Operations Research,2018

5. Machine learning for combinatorial optimization: A methodological tour d’horizon;Bengio;European journal of operational research,2021

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