Genetic Algorithm Driven by Translational Mutation Operator for the Scheduling Optimization in the Steelmaking-Continuous Casting Production

Abstract

The scheduling optimization of industrial processes is crucial for enhancing production capacity and minimizing energy consumption. In the realm of continuous casting, the expansion of the scheduling scale and the increasing number of scheduling objects pose challenges for genetic algorithms in swiftly generating optimal solutions that adhere to constraints. Prolonged scheduling decision times and difficulties in ensuring constant pouring constraints are critical issues that require urgent resolution in the continuous casting scheduling problem within steelmaking. This paper proposes a genetic algorithm driven by translational mutation operator for the scheduling optimization in the steelmaking-continuous casting production named TMGA. Incorporating continuous pouring information in the encoding process guarantees uninterrupted pouring during the casting stage. Furthermore, applying the translational mutation operator is instrumental in elevating the search efficiency for the global optimal solution, consequently diminishing scheduling decision times. To validate the effectiveness of the proposed approach, this study conducts a rigorous examination involving a numerical simulation case and two ablation experiments. The experimental results demonstrate the superior performance of TMGA compared to other methods.