Application of model predictive control for optimization of centralized and decentralized supply chain systems

Abstract

ABSTRACT This thesis studies Model Predictive Control (MPC) as an optimization tool for supply chain systems and compares its results on multi-product centralized and decentralized systems. The main challenge in supply chain optimization studies is to reduce the negative effects of rapid changes in demand. Previous work on application of MPC techniques onto supply chain systems aimed to keep inventory or customer satisfaction levels at a certain value, rather than optimizing the supply chain as a whole. In this study overall profit of the supply chain systems are optimized through MPC and compared with open-loop optimization results. Different MPC optimization horizon lengths are studied and MPC performance on centralized supply chains with different production size, initial inventory values and unpredictable demand change are observed. Semi- decentralized and decentralized supply chain systems are optimized with different MPC horizon lengths and their performance are studied. Performances of the decentralized configurations are compared with that of the centralized configuration while each configuration observes the same demand pattern and implements MPC with the same horizon length. It is observed that for both centralized and decentralized systems as the MPC horizon length increases, higher profits are obtained, yet a trade off must be made between high profits and low solution times. In the decentralized cases, the lack of information causes high bullwhip effect in the supply chains, increasing the costs and decreasing the profits. IV