Key Takeaways & Executive Findings
- •• The steel industry's intelligent transformation is driven by the need to reduce carbon emissions and enhance profitability, with production planning and scheduling optimization playing a pivotal role. • Current practices rely heavily on experience-driven manual decision-making, which is inadequate for the complex, multi-objective, and multiconstraint nature of steel production. • A novel framework for intelligent production planning and scheduling is proposed, leveraging data- and knowledge-driven decision-making and scenario adaptability to respond dynamically to real-time conditions. • Integrating artificial intelligence and advanced optimization methodologies can significantly improve efficiency, cost-effectiveness, and environmental sustainability in steel manufacturing.
Abstract
In the context of reducing its carbon emissions, the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability. The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency, saving energy, reducing carbon emissions, and enhancing quality. However, current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems, which are inadequate to meet the complex requirements of the industry. This study explores the current situation in production planning and scheduling, analyzes the characteristics and limitations of existing methods, and emphasizes the necessity and trends of intelligent systems. It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field. A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel production. To overcome these challenges, a novel framework for intelligent production planning and scheduling is proposed. This framework leverages data- and knowledge-driven decision-making and scenario 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 improves the efficiency, cost-effectiveness, and environmental sustainability of steel manufacturing.
1. Introduction
The steel industry is a fundamental sector that plays a crucial role in supplying essential materials. Steel manufacturing is characterized by lengthy and complex processes, high resource and energy consumption, challenging production organizations, and strict environmental protection standards, particularly regarding carbon emissions. Production planning and scheduling are of great significance in the steel industry. They are crucial not only for improving production efficiency but also have an impact on various production and operational indicators, such as energy efficiency, carbon footprint, cost reduction, efficiency enhancement, and market competitiveness. Effective steel production planning and scheduling can optimize resource utilization, coordinate production processes, and enhance overall enterprise profitability.
As a representative of the process manufacturing sector, steel production planning and scheduling constitute the operational protocol of the steel manufacturing process [1–2]. It directly affects the dynamic, orderly, coordinated, and continuous operation state and the effect of the iron-containing material flow propelled by energy flow. Moreover, it represents a multi-objective, complex-constrained combinatorial scheduling decision-making optimization problem in production optimization configurations when enterprises produce goods to meet external market requirements. Considering the external changeable market environment, multiproduction goals of the manufacturing process and process equipment, and different metallurgical processes and production organization requirements are necessary, making steel production planning and scheduling a very challenging technical problem.
According to a World Steel Association report, the Chinese steel industry accounts for more than half of the global steel production capacity. Additionally, the Chinese steel industry features advanced enterprise equipment levels, manufacturing technology across most products, and environmental protection control capabilities. Notably, the enterprise resource planning (ERP), manufacturing execution system (MES), and process control system (PCS) are commonly used information systems with integrated information management functions [3]. Large- and medium-sized iron and steel enterprises demonstrate strong capabilities in collecting and managing production planning and scheduling information. As these enterprises undergo digital transformation and upgrades, their informatization construction has gradually amassed a massive amount of data.
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Yongzhou Wang, Zhong Zheng, Liang Guo, Yongjie Yang, Shiyu Zhang, Xueying Liu, Xiaoqiang Gao (2025). Advancements in production planning and scheduling within steel manufacturing: A review and its intelligent development. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-025-3188-5
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Frequently Asked Questions
What is the main focus of the review on production planning and scheduling in steel manufacturing?
The review focuses on the current state, limitations, and intelligent development of production planning and scheduling in steel manufacturing, emphasizing the need for data- and knowledge-driven approaches to handle multi-objective and multiconstraint optimization problems.
Why is intelligent production planning and scheduling important for the steel industry?
Intelligent production planning and scheduling are crucial for improving production efficiency, saving energy, reducing carbon emissions, and enhancing quality, thereby supporting the industry's profitability and sustainability goals.
What are the limitations of current production planning and scheduling methods in steel enterprises?
Current methods are largely experience-driven and manual, supported by information systems, but they are inadequate to handle the complex, multi-objective, and multiconstraint characteristics of steel production, especially in dynamic market and production environments.
What novel framework is proposed in the paper?
The paper proposes a novel framework for intelligent production planning and scheduling that leverages data- and knowledge-driven decision-making and scenario adaptability, integrating artificial intelligence and advanced optimization methodologies to respond dynamically to real-time conditions.
How does the proposed framework improve steel manufacturing?
The framework improves efficiency, cost-effectiveness, and environmental sustainability by enabling dynamic responses to production conditions and market fluctuations, thus optimizing resource utilization and reducing waste.
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