Robust supply chain performance via Model Predictive Control
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...
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Veröffentlicht in: | Computers & chemical engineering 2009-12, Vol.33 (12), p.2134-2143 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 0098-1354 1873-4375 |
DOI: | 10.1016/j.compchemeng.2009.06.029 |