Predictive Control of Clustered Closed-Loop Supply Chains based on Multimodal Support Vector Machines

Haifeng Guo; Qinwen Yang

doi:10.54691/6t9gn782

Authors

Haifeng Guo
Qinwen Yang

DOI:

https://doi.org/10.54691/6t9gn782

Keywords:

Clustered Closed-loop Supply Chain; Multimodal Demand Forecasting; Support Vector Regression; Model Predictive Control; Cross-chain Inventory Coordination.

Abstract

To address inventory fluctuation and allocation coordination challenges faced by clustered closed-loop supply chains under demand uncertainty, this paper proposes an inventory allocation optimization method integrating demand forecasting with model predictive control (MPC) based on multimodal support vector regression. This approach utilizes numerical features such as historical sales data alongside sentiment features extracted from product reviews to establish a multimodal demand forecasting model. The forecast results are then embedded as exogenous inputs within an MPC control framework, enabling rolling optimization and coordinated allocation of inventory across multiple chains. Simulation comparisons with both basic MPC and unimodal SVR+MPC methods validate the improvements achieved by the multimodal SVR+MPC approach in demand forecasting accuracy, inventory fluctuation suppression, and system operational stability.

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