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CLC number: TP29; C939

On-line Access: 2010-04-28

Received: 2009-05-11

Revision Accepted: 2009-10-19

Crosschecked: 2010-03-29

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.5 P.394-400

10.1631/jzus.C0910270


Model predictive control with an on-line identification model of a supply chain unit


Author(s):  Jian Niu, Zu-hua Xu, Jun Zhao, Zhi-jiang Shao, Ji-xin Qian

Affiliation(s):  State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   xuzh@iipc.zju.edu.cn

Key Words:  Supply chain, Model predictive control, On-line identification, Optimization with constraint, Piecewise linear price


Jian Niu, Zu-hua Xu, Jun Zhao, Zhi-jiang Shao, Ji-xin Qian. Model predictive control with an on-line identification model of a supply chain unit[J]. Journal of Zhejiang University Science C, 2010, 11(5): 394-400.

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author="Jian Niu, Zu-hua Xu, Jun Zhao, Zhi-jiang Shao, Ji-xin Qian",
journal="Journal of Zhejiang University Science C",
volume="11",
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pages="394-400",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910270"
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%T Model predictive control with an on-line identification model of a supply chain unit
%A Jian Niu
%A Zu-hua Xu
%A Jun Zhao
%A Zhi-jiang Shao
%A Ji-xin Qian
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 5
%P 394-400
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%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910270

TY - JOUR
T1 - Model predictive control with an on-line identification model of a supply chain unit
A1 - Jian Niu
A1 - Zu-hua Xu
A1 - Jun Zhao
A1 - Zhi-jiang Shao
A1 - Ji-xin Qian
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 5
SP - 394
EP - 400
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910270


Abstract: 
A model predictive controller was designed in this study for a single supply chain unit. A demand model was described using an autoregressive integrated moving average (ARIMA) model, one that is identified on-line to forecast the future demand. Feedback was used to modify the demand prediction, and profit was chosen as the control objective. To imitate reality, the purchase price was assumed to be a piecewise linear form, whereby the control objective became a nonlinear problem. In addition, a genetic algorithm was introduced to solve the problem. Constraints were put on the predictive inventory to control the inventory fluctuation, that is, the bullwhip effect was controllable. The model predictive control (MPC) method was compared with the order-up-to-level (OUL) method in simulations. The results revealed that using the MPC method can result in more profit and make the bullwhip effect controllable.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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