The Application of the Second Generation Non-Dominated Sorting Genetic Algorithm in Multi-Project Human Resource Scheduling Management

With the development of the national economy, a large number of small and medium-sized enterprises have rapidly expanded in scale, and their internal structures have become increasingly complex. Traditional single project management is no longer suitable for enterprise management, demanding the need for improved project scheduling management to optimize resource utilization and enhance operational efficiency. Traditional single project management approaches are found to be inadequate due to the increased complexity and scale of small and medium-sized enterprises. These traditional methods struggle to efficiently manage resources and schedule tasks effectively within the context of multiple projects, leading to increased consumption and inefficiency. This study constructs a human resource scheduling model based on the second-generation non-dominated sorting genetic algorithm, aiming to determine the task sequence and time relationship during project implementation more effectively. By promoting the creation of the initial population and cross inheritance, the testing efficiency of the algorithm is improved. The proposed method significantly outperforms simple genetic algorithms in terms of accuracy, convergence speed, entropy measurement value, and distance value, thereby optimizing resource utilization and reducing consumption by 40%. The proposed model’s effectiveness is shown by using a project from a certain company as the test subject. This demonstrates how the second-generation non-dominated sorting genetic algorithm can make scheduling management of multiple projects better. It also helps to save resources and make overall project management more efficient.