Advancements in production planning and scheduling within steel manufacturing: A review and its intelligent development
Source abstract
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.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Scheduling and Optimization Algorithms · Manufacturing Process and Optimization · Advanced Manufacturing and Logistics Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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