Proactive scheduling of steelmaking-continuous casting with uncertain processing times under carbon emission reduction

Abstract

To solve the proactive scheduling problem of steelmaking-continuous casting with uncertain processing time, a fuzzy scheduling model is established to minimize the total completion time, maximum elapsed time, and carbon emission, considering machine energy consumption. An improved multi-objective beluga whale optimization algorithm (MOBWO-VNS) is proposed to solve it. In MOBWO-VNS, the process-machine matching rule is used to enhance the quality of initial solutions, an adaptive layered evolution strategy is designed to improve the convergence speed, and a variable neighborhood search strategy is introduced to strengthen the algorithm's local search capability. The diversity and convergence of the algorithm are verified by the benchmark function. A comparison experiment between deterministic scheduling and fuzzy scheduling is carried out using the actual production data of a steel plant, and the feasibility and robustness of fuzzy scheduling are verified. Large-scale scheduling evaluation experiments are designed using large-scale data, and the results show that the proposed model and algorithm reduce the total completion time, maximum elapsed time, and carbon emissions by 7.6 %, 19.2 %, and 2.2 %, respectively. Results show that this study can optimize the time and energy consumption targets, improve the production efficiency of enterprises, and provide strong support for the green transformation of steel enterprises.