Hungarian maximization model approach for optimizing human resource assignment in multi-site projects - Riandari, Fristi; Dalimunthe, Yulia Agustina; Ginting, Ramadhanu; Afifa, Rizky Maulidya; Afrisawati, Afrisawati (Jurnal Teknik Informatika C.I.T Medicom, 2025)
Digital transformation in project management demands the implementation of computational models that are able to handle the complexity of human resource (HR) allocation efficiently and objectively. This study examines the application of the Hungarian algorithm in the form of maximization as a computer science-based optimization solution to the HR assignment problem in multi-location projects. By constructing a benefit matrix calculated from weighted attributes such as technical expertise, experience, and location preference, this study implements linear transformations and matrix processing procedures using a numerical approach in Python. This digitalization process allows the system to perform assignment evaluation and allocation automatically and with high precision. Simulation results on a case study of five workers and five project locations show that the model produces optimal assignments with a total benefit score of 420. This model proves its effectiveness in solving polynomial assignment problems, while expanding the use of the Hungarian algorithm in the domain of applied computer science to support data-driven decision making. This study emphasizes the role of classical algorithms in supporting scalable and replicable digital solutions for modern HR management systems.