Research on planning and demand matching strategies for intelligent material supply chains under carbon constraints

• Published in: Cleaner Logistics and Supply Chain Vol. 15; p. 100222
• Main Authors: Xuwei, Shao, Jianfeng, Wu, Junping, Ge, Jianguo, Wang, Kairui, Hu, Yang, Qiu, Chunhua, Ju, Jiaming, Xu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2025; Elsevier
• Subjects: Carbon constraints; Carbon emissions; Matching strategy; Material supply chain; Program demand; Material supply chain; Matching strategy; Program demand; Carbon constraints; Carbon emissions
• ISSN: 2772-3909; 2772-3909
• DOI: 10.1016/j.clscn.2025.100222

For the coordination problem of planned demand in the intelligent material supply chain under carbon constraints, its supply chain network presents the characteristics of multi-source, multi-demand and high dispersion. This complex supply chain network makes it difficult for traditional optimization algorithms and path planning methods to effectively cope with the demand for low-carbon, efficient and flexible logistics. Therefore, starting from the matching fitness of both supply and demand sides, this paper constructs a dynamic matching decision framework that is more in line with the actual operation logic, and introduces a dynamic matching algorithm based on multi-factor stimulus value and response threshold to improve the adaptability and responsiveness of the model. Through multiple sets of numerical simulation experiments, the effectiveness and robustness of the proposed method in dealing with complex supply chain scenarios (such as multi-source and multi-demand node distribution) are verified. In terms of optimization performance, the proposed method is superior to traditional methods in core indicators such as operating efficiency, carbon emission control and supply and demand matching accuracy. The horizontal comparison results show that the proposed model has strong comprehensive advantages in the practice of green intelligent supply chain management, showing its theoretical innovation and wide application potential in the context of low-carbon transformation.