Presenting an Optimization Model for Human Resource Management Audit Knowledge Based on Genetic Algorithms

The aim of this research was to present an optimization model for a human resource management audit based on a genetic algorithm. This study is exploratory in nature due to the presentation of the model, and because its results are utilized by users, it is also considered practical. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed as a meta-heuristic method to solve nine simulation problems. The results obtained from this method were then compared with those from the epsilon constraint method. The relationship between the results indicates that the NSGA-II algorithm is capable of reaching optimal solutions in a shorter time compared to the epsilon method, although it has specific limitations when applied to large-scale problems. The results of solving the proposed mathematical model were demonstrated through nine simulations using the desired algorithms, which were implemented in GAMS and MATLAB software. The model considered in this research is a bi-objective model aimed at minimizing inter-cell movements and human resource management audit actions (cell formation), while maximizing the relationships among management audit operators, taking into account network considerations and the efficiency of operators in human resource allocation. This model not only enhances the efficiency of human resource management but also offers the flexibility to adapt to various organizational challenges by providing a new and effective approach. Therefore, the application of this optimization model can significantly improve performance and efficiency in human resource management, contributing to development and progress within the organizational environment.