Implementation of Distributed Model Predictive Controllers Based on Orthonormal Basis Functions to Increase Supply Chain Robustness

Today, companies need to make use of appropriate patterns such as supply chain management system to gain and preserve their positions in competitive world-wide market. supply chain is a large scale network consists of suppliers, manufacturers, warehouses, wholesalers, retailers and final customers which are in coordination with each other in order to transform products from raw materials into finished goods with optimal placement of inventory within the supply chain and minimizing operating costs in the face of demand fluctuations. model predictive control (mpc) is a widely used means on supply chains, but in presence of long delays and sudden disturbance changes (customer demand changes) , tuning of mpcs requires a time consuming trial-and-error procedure and system robustness will be highly decreased. in this paper, a control scheme is proposed to increase supply chain robustness. due to the large scale characteristic of supply chain, the model is divided into different subsystems and is controlled by distributed model predictive controllers. each subsystem model has been changed in which an integrated is imbedded, input and output changes are highly penalized in cost functions and laguerre orthonormal basis functions are added in mpc's structures and it will be shown that the supply chain robustness will be increased toward high changes in customer demand and toward long constant delays in distribution centres, also results will be compared to previous conventional mpcs applied on supply chains by other authors.