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

Jian NIU; Zu-hua XU; Jun ZHAO; Zhi-jiang SHAO; Ji-xin QIAN

doi:10.1631/jzus.C0910270

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

控制工程 | Updated：2022-08-31

- Model predictive control with an on-line identification model of a supply chain unit
- Model predictive control with an on-line identification model of a supply chain unit
- 信息与电子工程前沿（英文） 2010年11卷第5期页码：394-400
- Affiliations：
  
  State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/jzus.C0910270
  中图分类号：
- 收稿：2009-05-11，
  
  修回：2010-03-29，
  
  录用：2009-10-19，
  
  纸质出版：2010-05
- Accepted：
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Model predictive control with an on-line identification model of a supply chain unit[J]. 信息与电子工程前沿（英文）, 2010,11(5):394-400.

Jian NIU, Zu-hua XU, Jun ZHAO, et al. Model predictive control with an on-line identification model of a supply chain unit[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(5): 394-400.
Model predictive control with an on-line identification model of a supply chain unit[J]. 信息与电子工程前沿（英文）, 2010,11(5):394-400. DOI： 10.1631/jzus.C0910270.

Jian NIU, Zu-hua XU, Jun ZHAO, et al. Model predictive control with an on-line identification model of a supply chain unit[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(5): 394-400. DOI： 10.1631/jzus.C0910270.

摘要

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.

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