Advanced Control of Logistics in Supply Chain Management System toward Satisfying Customers

The aim of the article to optimize firstly the logistics of supply chain including manufacturing plants، warehouses، distributors and retailers by model-based predictive controller as centralized and secondly as decentralized under demand changes and thirdly the performance of which is compared. Model-based predictive control as a tool for advanced control consists of two parts: model identification at the time of sampling and optimization of the cost function. By online predictive control structure optimization on inputs in order to maintain safety inventory with the lowest possible operating cost is effectively feasible. Consequently، a measure of customer satisfaction is met. The considered model is an overhand differential model of the retailer to the manufacturer and with transmission delays. Given that the system state variables are not always available and it is uncertain system، a Kalman filter is used as observer. Finally، performance of methods is tested by simulations. Moreover، look ahead theory is examined in these simulations.