Research on Steelmaking-Continuous Casting Scheduling Based on the IDE-GWO Algorithm

The steelmaking-continuous casting process is the key to steel production. Efficient scheduling strategies can enhance production efficiency, improve machine utilization, and reduce production losses. Considering the characteristics of the steelmaking-continuous casting production scheduling, a method combining the Grey Wolf Optimizer (GWO) and an improved Differential Evolution (IDE) algorithm are proposed. First, a dual-layer encoding scheme generates a high-quality initial population of grey wolves. Next, mutation, crossover, and selection mechanisms from the improved Differential Evolution algorithm are utilized to prevent the algorithm from getting trapped in local optima, thereby enhancing convergence speed and computational accuracy. A production model is established based on the actual scheduling scenario of steelmaking-continuous casting, with the optimization objectives of minimizing the penalty value for waiting time, minimizing the penalty value for early completion, and minimizing the penalty value for delayed completion. The proposed algorithm is tested using classical benchmark functions, and the results demonstrate the superior performance of the IDE-GWO algorithm. Finally, the IDE-GWO algorithm is applied to solve the production scheduling model, and its effectiveness and superiority are validated through simulation experiments. The results indicate that the IDE-GWO algorithm exhibits faster solving speed and better solution accuracy.