Heating furnace scheduling with grey wolf optimization

Abstract To achieve energy-saving goals in the steel industry, this paper first analyzes the characteristics of billets and considers the impact of shape and size on heating time. Based on the billet heating specifications, an energy-efficient scheduling model is established with the furnace charging capacity difference rate and maximum completion time as the objective functions. An improved Grey Wolf Optimizer (GWO) algorithm, combined with real-valued encoding, is designed to solve this objective function. The model and algorithm’s effectiveness is verified through case studies. The results of the application examples show that the working time of each heating furnace is reduced by 12, 21, and 30 minutes, respectively. Compared to five other optimization algorithms, the improved GWO algorithm demonstrates superior performance. This model and algorithm meet the practical furnace charging requirements, improve furnace utilization, shorten the working time, and achieve energy-saving goals under specific production conditions, which is of significant importance for energy efficiency in steel enterprises.