M. Fatih TasgetirenDamla KizilayLevent KandillerTasgetiren, M. FatihKizilay, DamlaKandiller, Levent2025-10-06202423718366, 237183742371-83662371-837410.5267/j.jpm.2024.2.0022-s2.0-85185672739https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185672739&doi=10.5267%2Fj.jpm.2024.2.002&partnerID=40&md5=7b3f31108366cba0f26247d65604482ahttps://gcris.yasar.edu.tr/handle/123456789/8334https://doi.org/10.5267/j.jpm.2024.2.002This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters mainly the destruction size and temperature scale factor adaptively during the search process. Besides IGQ algorithms two different mathematical modeling tech-niques. One of these techniques is the constraint programming (CP) model which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessAlgorithms, Blocking Flowshop Scheduling, Problem, Q-learning-based Iterated Greedy, Reinforcement LearningAlgorithmsBlocking Flowshop SchedulingQ-Learning-Based Iterated GreedyProblemReinforcement LearningArticle