Two-stage Method for Solving Large-scale Hot Rolling Planning Problem in Steel Production

Abstract Hot rolling (HR) planning is a multi-objective and multi-constraint large-scale combinatorial optimization problem. A prize-collecting vehicle routing problem (PCVRP) based model for HR planning is presented according to the demand of HR. The model considers many practical constraints and reasonable arrangement of warm-up materials and staple materials, but considers objectives such as product quality, productivity and ensuring the normal production of downstream production lines etc. A two-stage method for solving HR planning, namely planning rolling units firstly according to the demand of single mode rolling unit and then planning rolling units according to the demand of mix mode rolling unit, is proposed. A simulated annealing (SA) and ant colony optimization (ACO) based hybrid algorithm is used for solving the first stage problem. A variable neighborhood tabu search (VNTS) algorithm is used for solving the second stage problem. Computational results show the effectiveness of the proposed method.