Optimisation of the entire sustainable supply chain for enterprise raw materials: cost reduction and efficiency enhancement via multi-objective dynamic programming and intelligent algorithms

In the context of global industrial green transformation, the raw material supply chain of building decoration panel enterprises faces key issues including supply deviations, transportation losses, and inventory backlog. This study establishes an end-to-end optimisation model covering supplier screening, order planning, transportation optimisation, and capacity coordination. The model innovatively integrates raw material heterogeneity with uncertainties in supply and transportation, and combines a multidimensional evaluation system with intelligent algorithms including autoregressive neural networks and particle swarm optimisation to achieve dynamic optimisation. Empirical validation is carried out using 240 weeks of order and supply data from 402 suppliers, together with loss rate data from eight transporters. The results show a 31.28% increase in enterprise production capacity. This study establishes a transferable methodology for resource-intensive manufacturing enterprises, enhances their ability to resist supply chain uncertainties, and achieves cost reduction and efficiency improvement. This approach reduces per-unit product raw material consumption and transportation losses, improves resource utilization efficiency, promotes green and low-carbon development while maintaining stable production and logistics scales, and provides quantifiable support for alignment with SDG 9, 12, and 13.