School of Economics and Management,University of Science and Technology Beijing
Supported by National Natural Science Foundation of China (No. 71701016，71231001), Humanity and Social Science Youth foundation of Ministry of Education of China (No. 17YJC630143), Beijing Natural Science Foundation (No. 9174038), and the Fundamental Research Funds for Central Universities (No. FRF-BD-20-16A)
Reheating furnace production is one of the important procedures that affect the utilization rate of hot rolling mills and the quality of rolling plans. By analyzing the influence of reheating furnace on hot rolling production, two key factors of slab, standard time in furnace and discharge temperature, are extracted. The integer programming model of hot-rolled slab rolling plan is established, and an adaptive neighborhood search algorithm is proposed. Constraint satisfaction strategy, adaptive search strategy and reverse learning neighborhood search strategy are designed in the algorithm. Two value selection rules of the constraint satisfaction strategy are designed for target characteristics and furnace factors to generate high-quality initial solutions; By using the adaptive search strategy, neighborhood structure can be autonomously selected and neighborhood search can be terminated autonomously, the neighborhood structure selection process and algorithm convergence speed are effectively optimized; the reverse learning neighborhood search strategy is based on the reverse learning technology to enhance the diversity of the solution space and improve the global search ability. Based on actual production data, experiments of various scales are designed to verify the effectiveness of the algorithm.