Abstract—Supply chain management system is a network of facilities and distribution entities: suppliers, manufacturers, distributors, retailers. The control system aims at operating the supply chain at the optimal point despite the influence of demand changes. In fact, the main objectives of the control strategy for the supply chain network can be summarized as follows: maximize customer satisfaction, and minimize supply chain operating costs. In this work, based on the fact that past and present control actions affect the future response of the system, a receding time horizon optimal control or model predictive control (MPC) is efficient. Also since a centralized control scheme may not suitable or even possible for technical or commercial reasons, it is useful to have decentralized control schemes. In this method, each node completely by a constrained decentralized MPC optimizes locally for its own policy. At each time period, the first decentralized predictive control action in the calculated sequence is implemented until MPC process complete. So As locally constrained predictive controllers applying to a supply chain management system consist of two plant, three warehouses, four distribution centers and four retailers.
Index Terms—Supply chain management, Demand, Optimal control, Predictive control.
M. Miranbeigi, is with the Department of Electrical Engineering, Iran University of Science and Technology, Narmak ,Tehran, Iran, Phone : +9833040306; E-mail : email@example.com.
A. A. Jalali, is with the Department of Electrical Engineering, Iran University of Science and Technology, Narmak ,Tehran, Iran, Phone : +9877451504; E-mail : firstname.lastname@example.org.
A. Miranbeigi, is with the Department of mechanical Engineering, Rajaee University of Science and Technology, Narmak ,Tehran, Iran, Phone : +9833040306; E-mail : email@example.com.
Cite: Mohammad Miranbeigi, Aliakbar Jalali, Ali Miranbeigi, " A Constrained Inventory Level Optimal Control on Supply Chain Management System," International Journal of Innovation, Management and Technology vol. 1, no. 1, pp. 69-74, 2010.