Research on Model Predictive Control for Inventory Management in Decentralized Supply Chain System

In a decentralized supply chain system, it is very important to forecast the changes in the market in order to maintain an inventory level that is just enough to satisfy customer demand. A optimization-based control approach for supply chain networks is presented. The control strategy applies model predictive control principles to the entire supply chain networks, and supply chains whose dynamic behavior can be adequately represented by fluid analogies. A simultaneous perturbation stochastic approximation (SPSA) optimization algorithm is presented as a means to obtain optimal tuning parameters for the proposed policies. The SPSA technique is capable of optimizing important system parameters, such as safety stock targets and controller tuning parameters. Simulated results exhibit good dynamic performance and financial benefit under maintaining robust operation in a decentralized supply chain system.