https://journals.bohrpub.com/index.php/bijomrp/issue/feed 2026-08-29T06:57:21+00:00 Jayanthi Roselin editor@bohrpub.com Open Journal Systems <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> https://journals.bohrpub.com/index.php/bijomrp/article/view/1024 2026-06-26T06:54:08+00:00 Phong Nguyen Nhu nnpng@hcmut.edu.vn Kim Ngan Nguyen Thi nnphong@hcmut.edu.vn Tu Anh Nguyen Nhu nnphong@hcmut.edu.vn

<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>

2026-01-09T00:00:00+00:00 Copyright (c) 2026 Phong Nguyen Nhu, Kim Ngan Nguyen Thi, Tu Anh Nguyen Nhu https://journals.bohrpub.com/index.php/bijomrp/article/view/1058 2026-08-29T06:57:21+00:00 Phong Nguyen Nhu nnphong@hcmut.edu.vn Thu Anh Le Hoang nnphong@hcmut.edu.vn Tu Anh Nguyen Nhu nnphong@hcmut.edu.vn

<p>In inventory planning, the Dynamic Lot-sizing (DLS) problem is a challenging problem characterized by time-varying demand and quantity-varying costs. Optimization methods using mathematical models have difficulty solving this problem due to its combinatorial nature and dynamic characteristics. Genetic algorithm (GA) is a meta-heuristic algorithm that can search for near-optimal solutions in a large solution space, thereby effectively solving this problem. In this study, the mathematical model of the DLS problem has been constructed. Based on the model, a GA algorithm is developed and used to solve the DLS problem with the objective to minimize the total inventory cost. The parameters of the GA algorithm are optimized by using Design of Experiment (DOE). The results show that GA is better than the currently used heuristic algorithm, and GA with DOE is better than GA.</p>

2026-02-03T00:00:00+00:00 Copyright (c) 2026 Phong Nguyen Nhu, Thu Anh Le Hoang, Tu Anh Nguyen Nhu