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

作　　者：Jian NIU Zu-hua XU Jun ZHAO Zhi-jiang SHAO Ji-xin QIAN 

机构地区：[1]State Key Lab of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2010年第5期394-400,共7页浙江大学学报C辑（计算机与电子（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos.60804023,60934007,and 60974007);the National Basic Research Program (973) of China (No.2009CB320603)

摘　　要：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.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 pur- chase 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.

关 键 词：Supply chain Model predictive control On-line identification Optimization with constraint Piecewise linear price 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置] C939[自动化与计算机技术—控制科学与工程]