Supply Chain Management Systems Advanced Control: MPC on SCM

A supply chain is a network of facilities and distribution entities (suppliers, manufacturers, distributors, retailers) that performs the functions of procurement of raw materials, transformation of raw materials into intermediate and finished products and distribution of finished products to customers. Between interconnected entities, there are two types of process flows: information flows, e.g., an order requesting goods, and material flows, i.e., the actual shipment of goods (Figure 1). Key elements to an efficient supply chain are accurate pinpointing of process flows and timing of supply needs at each entity, both of which enable entities to request items as they are needed, thereby reducing safety stock levels to free space and capital. The operational planning and direct control of the network can in principle be addressed by a variety of methods, including deterministic analytical models, stochastic analytical models, and simulation models, coupled with the desired optimization objectives and network performance measures (Beamon, 1998). The merit of model predictive control (MPC) is its applications in multivariable control in the presence of constraints. The success of MPC is due to the fact that it is perhaps the most general way of posing the control problem in the time domain. The use a finite horizon strategy allows the explicit handling of process and operational constraints by the MPC (Igor, 2008). In a recent paper (Perea et al., 2003), a MPC strategy was employed for the optimization of production/ distribution systems, including a simplified scheduling model for the manufacturing function. The suggested control strategy considers only deterministic type of demand, which reduces the need for an inventory control mechanism (Seferlis et al., 2004:Kapsiotis et al., 1992). For the purposes of our study and the time scales of interest, a discrete time difference model is developed(Tzafestas, 1997). The model is applicable to multi echelon supply chain networks of arbitrary structure. To treat process uncertainty within the deterministic supply chain network model, a MPC approach is suggested (Wang et al., 2005:Chopra et al., 2004). Typically, MPC is implemented in a centralized fashion (Wang et al., 2005). The complete system is modeled, and all the control inputs are computed in one optimization problem. In large scale applications, such as power systems, water distribution systems, traffic systems, manufacturing systems, and economic systems, such a centralized control scheme may not suitable or even possible for technical or commercial reasons (Sarimveis et al., 2008), it is useful to have distributed or decentralized control schemes, where local control inputs are computed using local measurements and reduced order models of the local dynamics. The