A model predictive control strategy for supply chain management in semiconductor manufacturing under uncertainty

Author(s):  
W. Wang ◽  
D.E. Rivera ◽  
K.G. Kempf ◽  
K.D. Smith
2013 ◽  
Vol 46 (9) ◽  
pp. 1608-1613 ◽  
Author(s):  
Dongfei Fu ◽  
Clara Mihaela Ionescu ◽  
El-Houssaine Aghezzaf ◽  
Robin De Keyser

2011 ◽  
Vol 421 ◽  
pp. 548-552
Author(s):  
Hai Dong ◽  
Wei Ling Zhao ◽  
Yan Ping Li

A novel predictive control strategy is applied to dynamic supply chain management under networked manufacturing. The optimisation-based control scheme aims at a complete management framework for production-inventory systems that is based on model predictive control (MPC) and on a neural network time series forecasting model. The model proves to be very useful in production-inventory systems that is enough to keep production and inventory balance. A move suppression term that penalises the rate of change in the transported quantities through the network increases the system’s robustness. The results show that the proposed scheme can improve significantly the performance of the production-inventory system, due to the fact that more accurate predictions are provided to the formulation of the MPC optimization problem in real time.


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