Production planning and scheduling in steel manufacturing process：a review and its intelligent development

In the context of the low-carbon development of global manufacturing industry, the Chinese steel industry is currently undergoing an intelligent transformation with the aim of enhancing both profitability and sustainability. The optimization of production planning and scheduling plays a pivotal role in realizing these objectives, influencing energy efficiency, production efficiency, costs, and carbon emissions. However, current practices in steel enterprises largely depend on manual experience-driven approaches, and these methods are inadequate to meet the complex requirements of the industry. This paper explores the current situation of production planning and scheduling, exposes the drawbacks of manual methods and emphasizes the necessity of intelligent systems. This paper surveys extant literature on production planning and scheduling within steel enterprises, and analyzes the theoretical progressions and practical challenges linked to combinatorial and sequencing optimization within this domain. A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective, multi-constraint characteristics of steel production. To surmount these challenges, a novel framework for intelligent production planning and scheduling is proposed. This framework leverages data-driven decision-making and scene adaptability, enabling the system to respond dynamically to real-time production conditions and market fluctuations. By integrating artificial intelligence and advanced optimization methodologies, the proposed framework strives to elevate the efficiency, cost-effectiveness, and environmental sustainability of steel manufacturing.