A Discrete Growth Optimizer for Energy-Efficient Steelmaking-Refining-Continuous Casting Scheduling Problems

Iron and steel industry is a significant basic industry of national economy. Steelmaking-Refining-Continuous Casting (SRCC) is one of the bottlenecks of the iron and steel production process. SRCC scheduling problems are world-wide and NP-hard problems. SRCC scheduling problems considering energy saving are named Energy-Efficient SRCC (EESRCC) scheduling problems. Effective EESRCC scheduling algorithms would not only help to enhance the production efficiency, but also help to reduce the energy saving. This paper proposed a Discrete Growth Optimizer (DGO) to solve the EESRCC scheduling problems. Differ from the traditional GO, the proposed DGO is enhanced by incorporating five strategies. More specifically, a population initialization heuristic is designed to generate a relatively ‘good’ initial population. The control based local search is devised to enhance the intensification and diversification abilities of the proposed DGO. The restricted local search is designed to further improve the three best solutions found so far. The enhanced learning phase is devised to learn from the three best solutions found so far and elite solutions in the population. The multi-type reflection phase is developed to further enhance the solutions in the population. The effectiveness of the DGO has been verified by the experiments.