Stochastic MPC for supply chain management using MCMC approaches

Author

Zhuge, Jinjun ; Xue, Meisheng ; Li, Zukui

Author_Institution

Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China

fYear

2010

fDate

26-28 May 2010

Firstpage

167

Lastpage

172

Abstract

In order to efficiently manage a single echelon supply chain under stochastic disturbance and probabilistic constraints, we propose a stochastic model predictive control (SMPC) framework and implement the Markov Chain Monte Carlo (MCMC) algorithm to solve stochastic programming problems in a receding horizon scheme. The generalized autoregressive conditional heteroskedasticity (GARCH) model is adopted for demand forecasting. Simulation results show that the new approaches outperform the standard MPC in mitigating the production fluctuating and reducing the integral absolute error (IAE) of the inventory.

Keywords

Markov processes; Monte Carlo methods; autoregressive processes; demand forecasting; predictive control; stochastic processes; stochastic programming; stock control; supply chain management; MCMC approach; Markov Chain Monte Carlo algorithm; demand forecasting; generalized autoregressive conditional heteroskedasticity model; integral absolute error; probabilistic constraint; single echelon supply chain management; stochastic MPC; stochastic disturbance; stochastic model predictive control; stochastic programming problems; Automation; Demand forecasting; Monte Carlo methods; Predictive control; Predictive models; Production systems; Stochastic processes; Supply chain management; Supply chains; Uncertainty; Demand Forecasting; Inventory Control; Markov Chain Monte Carlo; Stochastic Model Predictive Control; Supply Chain Management;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5499098

Filename

5499098