Charge Plan Model for Steelmaking-Continuous Casting Section

Pursuing the intellectualization of a steelmaking plant and developing a charge plan of the steelmaking-continuous casting section are critical in metallurgy engineering. Herein, we aim to develop a charge plan model based on the operation of the steelmaking-continuous casting section to minimize the penalty values of residual materials; of a contract not selected and the penalty values that is caused by the difference in steel grades, the width and the delivery time between slabs in the same charge. We introduce an improved elitist genetic algorithm (IEGA), define the matching chromosome coding and decoding strategies, and suggest improving the selection, crossover, and mutation operators. Finally, we verify the proposed model and algorithm on the production data of a real enterprise. We clarify the applicability of developing a charge plan based on model analysis and demonstrate the effectiveness of the IEGA through algorithm analysis.

Optimum Charge Plan of Steelmaking Continuous Casting Based on the Modified Multi-Objective Evolutionary Algorithm

The charge plan is a basic problem of the metallurgical industry. Through a comprehensive consideration of the process constraints and production costs, charge plan is a typical multi-objective optimization problem. A multi-objective optimization model of charge plan was built in this paper. As direct encoding is hard to be used in the many constraints problem case, a new method of generate feasible solutions based on constraint satisfaction was proposed. A novel adaptive grid algorithm was also proposed to improve the current multi-objective evolutionary algorithm’s population maintain strategy. Finally, the proposed multi-objective charge plan model was solved, and more meaningful solutions for this problem were obtained.