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

作     者：Zhou, Yaluo Xiang, Hengju Zhou, Wenzhe Liu, Wenguang Zhang, Ruicheng 

作者机构：North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Shougang Jingtang Linited Iron & Steel Co Ltd Tangshan 063200 Peoples R China 

出 版 物：《CHEMICAL ENGINEERING RESEARCH & DESIGN》 (Chem. Eng. Res. Des.)

年 卷 期：2024年第212卷

页      面：421-433页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 

主　　题：Steelmaking-continuous casting Uncertain processing time Proactive scheduling Energy consumption Improved multi-objective beluga whale optimization 

摘      要：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.