A Model Predictive Control and Time Series Forecasting Framework for Supply Chain Management
Model Predictive Control has been previously applied
to supply chain problems with promising results; however hitherto
proposed systems possessed no information on future demand. A
forecasting methodology will surely promote the efficiency of
control actions by providing insight on the future. A complete supply
chain management framework that is based on Model Predictive
Control (MPC) and Time Series Forecasting will be presented in this
paper. The proposed framework will be tested on industrial data in
order to assess the efficiency of the method and the impact of
forecast accuracy on overall control performance of the supply chain.
To this end, forecasting methodologies with different characteristics
will be implemented on test data to generate forecasts that will serve
as input to the Model Predictive Control module.
[1] M. Morari, .JH. Lee, "Model predictive control: Past, present, and
future", Computers & Chemical Engineering, vol. 23, 1999, pp. 667-
682.
[2] G. Kapsiotis, S. Tzafestas, "Decision making for inventory/production
planning using model-based predictive control", in: S. Tzafestas, P.
Borne, L. Grandinetti (Eds.), Parallel and distributed computing in
engineering systems, Amsterdam: Elsevier, 1992, pp. 551-556.
[3] E. Perea Lopez, B. E. Ydstie, I. Grossmann, "A model predictive control
strategy for supply chain management", in Computers & Chemical
Engineering, vol. 27, 2003, pp. 1201-1218.
[4] M. W. Braun, D. E. Rivera, M. E. Flores, W. M. Carlyle, K. G. Kempf,
"A model predictive control framework for robust management of multiproduct,
multi-echelon demand networks", in Annual Reviews in
Control, vol. 27, pp. 229-245.
[5] P. H. Lin, S. S. Jang, D. S. H. Wong, "Predictive control of a
decentralized supply chain unit". Industrial Engineering & Chemistry
Research, vol. 44, 2005, pp. 9120-9128.
[6] G. E. P. Box, G. M. Jenkins & G. C. Reinsel, "Time series analysis :
forecasting and control", 3rd ed., Englewood Cliffs, New Jersey,
Prentice Hall, 1994.
[1] M. Morari, .JH. Lee, "Model predictive control: Past, present, and
future", Computers & Chemical Engineering, vol. 23, 1999, pp. 667-
682.
[2] G. Kapsiotis, S. Tzafestas, "Decision making for inventory/production
planning using model-based predictive control", in: S. Tzafestas, P.
Borne, L. Grandinetti (Eds.), Parallel and distributed computing in
engineering systems, Amsterdam: Elsevier, 1992, pp. 551-556.
[3] E. Perea Lopez, B. E. Ydstie, I. Grossmann, "A model predictive control
strategy for supply chain management", in Computers & Chemical
Engineering, vol. 27, 2003, pp. 1201-1218.
[4] M. W. Braun, D. E. Rivera, M. E. Flores, W. M. Carlyle, K. G. Kempf,
"A model predictive control framework for robust management of multiproduct,
multi-echelon demand networks", in Annual Reviews in
Control, vol. 27, pp. 229-245.
[5] P. H. Lin, S. S. Jang, D. S. H. Wong, "Predictive control of a
decentralized supply chain unit". Industrial Engineering & Chemistry
Research, vol. 44, 2005, pp. 9120-9128.
[6] G. E. P. Box, G. M. Jenkins & G. C. Reinsel, "Time series analysis :
forecasting and control", 3rd ed., Englewood Cliffs, New Jersey,
Prentice Hall, 1994.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55200", author = "Philip Doganis and Eleni Aggelogiannaki and Haralambos Sarimveis", title = "A Model Predictive Control and Time Series Forecasting Framework for Supply Chain Management", abstract = "Model Predictive Control has been previously applied
to supply chain problems with promising results; however hitherto
proposed systems possessed no information on future demand. A
forecasting methodology will surely promote the efficiency of
control actions by providing insight on the future. A complete supply
chain management framework that is based on Model Predictive
Control (MPC) and Time Series Forecasting will be presented in this
paper. The proposed framework will be tested on industrial data in
order to assess the efficiency of the method and the impact of
forecast accuracy on overall control performance of the supply chain.
To this end, forecasting methodologies with different characteristics
will be implemented on test data to generate forecasts that will serve
as input to the Model Predictive Control module.", keywords = "Forecasting, Model predictive control, production
planning.", volume = "2", number = "3", pages = "304-5", }