https://journals.bohrpub.com/index.php/bijomrp <p><strong>BOHR International Journal of Operations Management Research and Practices (BIJOMRP)</strong> is an open-access peer-reviewed journal that publishes articles that contribute new results in all the areas of Operations Management Research and Practices. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works, and industrial experiences that describe significant advances in this area.</p> BOHR Publishers en-US BOHR International Journal of Operations Management Research and Practices 2583-6420 <p>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.</p> https://journals.bohrpub.com/index.php/bijomrp/article/view/1024 <p>Flow shop scheduling (FSS) problems are nondeterministic polynomial (NP)-hard combinatorial optimization problems. It is quite difficult to achieve an optimal solution for real-size problems with mathematical modelling approaches. Meta-heuristics algorithms, like genetic algorithm (GA) and tabu search (TS), play a major role in searching for near-optimal solutions for NP-hard optimization problems. The scheduling method in the case study is not effective; the total tardiness time of orders is rather high. This paper develops a genetic algorithm and tabu search (GATS) algorithm for solving the real FSS problem, with the objective to schedule orders more effectively than the current earliest due date (EDD) model. The GATS algorithm is a hybrid meta-heuristic model, combining GA and TS. In the model, GA is used as the platform for global search, and TS is used to support GA in local search. The performance of the algorithm is compared with the heuristic EDD model being used. The result shows that the algorithm is a good approach for FSS problems. However, the factors of the algorithm are chosen empirically, so the results are only better than the results of the current approach, and not really satisfactory. Future research is to use the experimental design to identify the algorithm’s factors to obtain better results.</p> Phong Nguyen Nhu Kim Ngan Nguyen Thi Tu Anh Nguyen Nhu Copyright (c) 2026 Phong Nguyen Nhu, Kim Ngan Nguyen Thi, Tu Anh Nguyen Nhu https://creativecommons.org/licenses/by/4.0 2026-01-09 2026-01-09 5 1 1 10 10.54646/bijomrp.2026.38