Damla YukselLevent KandillerMehmet Fatih TasgetirenYüksel, DamlaTaşgetiren, Mehmet FatihKandiller, Levent2025-10-0620242210-65022210-651010.1016/j.swevo.2024.1016172-s2.0-85195865594http://dx.doi.org/10.1016/j.swevo.2024.101617https://gcris.yasar.edu.tr/handle/123456789/6736https://doi.org/10.1016/j.swevo.2024.101617Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly a deterministic mixed-integer linear programming (MILP) model is provided. Then Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BCIGQL). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). Moreover the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS) Bi-Criteria Iterated Greedy Algorithm (BC-IG) Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IGALL) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment performed on 480 problem instances of Vallada et al. (2015) which is known as the VRF benchmark set indicates that the BC-BIHQL and the BC-IGQL algorithms outperform the BC-GLS BC-IG BCIGALL and BC-VBIH algorithms in comparative performance metrics. More specifically the proposed BC-BIHQL and BC-IGQL algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time both are competitive with each other on the benchmark problems. Moreover the BC-IGQL algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG BC-IGALL and BC-VBIH algorithms in small and large datasets. Similarly The BC-BIHQL algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG BC-IGALL and BC-VBIH algorithms in small and large datasets respectively. This means that among all the features that have been compared the Qlearning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives such as makespan & the number of early jobs to overcome various industries' problems.Englishinfo:eu-repo/semantics/closedAccessBi-criteria scheduling problems, No -wait flowshop scheduling problem, Makespan, Total flow time, Mixed -integer linear programming, Bi-criteria heuristic optimizationTOTAL WEIGHTED TARDINESS, MULTIOBJECTIVE ELECTROMAGNETISM ALGORITHM, ITERATED GREEDY ALGORITHM, DEPENDENT SETUP TIMES, SCHEDULING PROBLEM, SINGLE-MACHINE, LOCAL SEARCH, MEMETIC ALGORITHM, OPTIMIZATION, SUBJECTMakespanBi-Criteria Heuristic OptimizationMixed -Integer Linear ProgrammingBi-Criteria Scheduling ProblemsNo-Wait Flowshop Scheduling ProblemMixed-Integer Linear ProgrammingNo -Wait Flowshop Scheduling ProblemTotal Flow TimeArticle