Improved genetic algorithm for optimizing flexible workshop scheduling

In automated production lines, flexible workshop scheduling is essential for optimizing resource allocation and ensuring efficient production. This study develops an efficient scheduling strategy to enhance the distribution of processing and handling tasks. Addressing the issues of local optima and slow convergence in traditional Genetic search algorithms, we introduce an improved Genetic algorithm (GA) that dynamically adjusts the crossover and mutation rates. Additionally, it sorts individuals based on their fitness during the crossover process, significantly improving the quality and efficiency of solutions. Simulation results demonstrate that the improved algorithm significantly outperforms traditional methods in task completion, reducing the time from 198 to 146 unit times, a decrease of 26%. Furthermore, the algorithm exhibits faster convergence and superior optimization capabilities.