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Robust supply chain performance via Model Predictive Control
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DOI：10.1016/j.compchemeng.2009.06.029.png)
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Abstract
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This paper presents a novel robust Model Predictive Control (MPC) method for real-time supply chain optimization under uncertainties. This method optimizes the closed-loop economic performance of supply chain systems and addresses different sources of uncertainties located external to and within the feedback loop. The future system behavior is predicted by a closed-loop model, which includes both the open-loop system model and a controller model described by an optimization problem. The robust MPC formulation involves the solution of a constrained. bi-level stochastic optimization problem. which is transformed into a tractable problem involving a limited number of deterministic conic optimization problems solved reliably using an interior point method. The robust controller is applied to a real industrial multi-echelon supply chain optimization problem. and its performance is shown to reduce stock-outs without excessive inventories. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Supply chain
Robust MPC
Stochastic optimization
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