• 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.