Inventory Control and Simulation Optimization in Supply Chain Network Based on MPC

Article Preview

Abstract:

The control approach applies multivariable model-predictive control principles to the entire network. The optimization scheme based on predictive-control aims at adjusting the decision variables in the supply chain to satisfy the customer orders with the least operating cost over a specified rolling time horizon using a detailed difference model of the system. Dedicated feedback controllers are utilized to maintain product inventory at all nodes of the supply chain network within pre-specified target levels that are subsequently embedded within the optimization control framework. The PID control policy is applied to promoting the predictive control effect, and the comparative simulations of the system under both PID and non-PID control are exerted. Simulated results prove that the inventory control system with the proposed MPC optimizations show good dynamic performance. But the MPC approach combined with PID control makes better effect on inventory predicted control in the supply chain network.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1718-1723

Citation:

Online since:

July 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Qin, S.J., & Badgwell, T.A. A survey of industrial model predictive control technology. Control Engineering Practice, 11, 733-764 (2003).

DOI: 10.1016/s0967-0661(02)00186-7

Google Scholar

[2] Pyke, D.F., &Cohen, M.A. Multiproduct integrated production distribution system. European Journal of Operations Research, 74, 18 (1994).

Google Scholar

[3] Kapsiotis, &Kyriannakis. Model-based predictive control for generalized production planning problems. Computers in Industry, 34, 201-210 (1997).

DOI: 10.1016/s0166-3615(97)00055-9

Google Scholar

[4] Grossmann, II. A model predictive control strategy for supply chain management. Computers and Chemical Engineering, 27, 1201-1218 (2003).

DOI: 10.1016/s0098-1354(03)00047-4

Google Scholar

[5] Lagodimos, A.G. Multi-echelon systems: a service measure perspective. European Journal of Operations Research, 95, 241(1996).

DOI: 10.1016/s0377-2217(96)00120-8

Google Scholar

[6] Box, G.E.P., &Jenkins, G.M. Time series analysis: forecasting and control Oakland: Holden Day (1976).

Google Scholar

[7] Seferlis, P., & Giannelos, N. F. An optimization based control strategy for multi-echelon supply chain networks. Paper 511 in the 53rd Canadian Chemical Engineering Conference. Hamilton, Ont., Canada (2003).

DOI: 10.1016/j.compchemeng.2004.02.022

Google Scholar