mohammad miranbeigi

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.

Nowadays, production/distribution/inventory management as supply chain management system has replaced the traditional inventory management systems based on empirical decision of the manager. In the past half century, several mathematical tools have been used in modeling and control of supply chain management issue. In regard to advantages of model-based predictive control (MPC) in dealing with control of multivariable systems and its online structure, this method is efficient for utilization on inventory management systems. In this article, a predictive control with strategies to increase the robustness of production/distribution/inventory management is used, and a separate control is designed for any echelon till an initial ensure for centralized control is achieved. Robustness mechanisms are also studied in simulations.

In the large scale applications, sometimes it is essential that the control theories be distributed or decentralized. In this paper, regarding large scale of power systems and advantages of model predictive control (MPC), a constraint predictive control method that is called “Receding Horizon Control (RHC)”, used for load frequency control in a two-area power system with two cooperating methods of agents. The main advantage of decentralized control is complexity reduction in computations. Moreover, in the application of centralized control, online computation of inputs (high dimensions) is complex, and non-flexible, and impractical. Finally using of simulations, centralized and decentralized control methods, cooperative methods are compared under a variety of disturbances.

Supply chain management system (SCM) is a network of suppliers and manufacturers and warehouses and distributors and retailers, with large delay times. The control system aims at operating the supply chain at the optimal point despite the influence of demand changes. In this paper, an information coupled supply chain management system model based on “Beer Game Theory” was used and was developed to an supply chain management consist of supply, manufacture, warehouse, distribution, retailer units. Then a centralized constrained model predictive controller applied on that. Also a move suppression term added to cost function, which increased system robustness toward changes on demands.