Data-Driven SVM-Based Predictive Control for Clustered Closed-Loop Supply Chains

Aiming to address the inventory fluctuation and inventory management problems caused by demand uncertainty in the clustered closed-loop supply chain, this paper proposes a method of optimizing the inventory state of the clustered closed-loop supply chain by support vector machine (SVM) combined with predictive control. Based on the consideration of the inventory state of each node in the supply chain, the article carries out demand prediction by support vector machine, and then establishes a cluster closed-loop supply chain system model by combining with predictive control theory to realize the regulation of inventory. Simulation results show that compared with the traditional method, the method proposed in this paper can achieves smoother production and inventory curves, effectively reduce the bullwhip effect in the supply chain, and improve the robustness and response speed of the supply chain.