Multi-resource Balanced Scheduling Optimization Based on Self-adaptive Genetic Algorithm

With a discussion on the slow convergence in the traditional Genetic Algorithm for scheduling problems and the improvement of the crossover operator and mutation operator in the process of optimization, this paper proposes a new method to use the Self-adaptive GA to resolve the “Fixed Time Limit for a Project” problem of Multi-Resource Balanced Scheduling Optimization, with a goal of the balanced resources under the fixed time. Comparison of experimental results shows that the Self-adaptive GA has better evolution and self-adaptivity than the traditional Genetic Algorithm on the “Fixed Time Limit for a Project, Resources Balanced” problem of Multi-Resource Balanced Scheduling Optimization.