Supply Chain Network Optimize Strategy Based On MPC Approach

During the recent decades, with the economy's Globalization, world wide resourcing, producing and purchasing, the market environment has becoming more and more complicated. When facing the rapid cycle of products, pressure of higher customer service degree, competition makes every enterprise within the supply chain modifies its traditional strategy to keep up with the changing market. To each of its supply chain manager, the process of decision making is not a simple work; they need to take account in all the possible exterior changes e.g. market damping, to adjust their producing schedule and inventory policy. Therefore, an advanced, practical solution or decision making approach is essential.Model Predictive Control (MPC) has prevailed in industry practise. Its robustness and moving horizon features are suitable for optimize control within complex system. However, because the complexity of supply chain is enormous compared with normal industrial process, application of MPC in supply chain is rare and difficult.Based on the former achievements, an integrated optimization scheme for supply chain on-line operation-decision-making support issue is proposed based on the model predictive control. Its key points conclude:1) Most of the present supply chain models are only qualitative or partial. In this paper, the research is focusing on a dynamic quantitative model of the whole supply chain system to obtain an integrative decision-making solution schema. Furthermore, a revision to the cost function and optimization objective is presented to ensure that the optimal solution minimizes overall costs, while satisfies a certain customer demand satisfactory (CDS) level and constraints;2) The model predictive control algorithm design is accomplished and implemented for supply chain operation based on the integrated model and the linear cost function keeping the penalties to the control variables variation. A simulation based on Matlab & Simulink framework is established to prove the efficiency of this algorithm. Simulation results illustrate that the application of MPC strategy brings in great improvement on both profit and CDS level magnification with determinate demands as well as uncertain demands. Also the algorithm shows great robustness.3) Facing the fact that serious uncertainty exists within the supply chain, the Value-at-Risk (VaR) inventory management strategy and Risk-sensitive filter are introduced into the inventory calculation, in order to confront demand uncertainty, thus CDS level & demand uncertainty can be handled without accurate measurement error model. Furthermore, by bringing the VaR inventory management strategy into the overall supply chain optimization, enhance the model predictive control strategy, and project a general dynamic solution schema for supply chain operation under uncertain demands based on the enhanced MPC.A simulation based on Matlab & Simulink framework is established to prove the efficiency of this algorithm. Simulation results illustrate and prove that the applications of MPC strategy as well as enhanced strategy are effective on profit and customer satisfaction improvement under uncertainty, with satisfied profit magnification. (section 5.2.3). Then the paper compares the robustness of MPC and enhanced MPC, and shows the latter behaves a persistent ascending trend as uncertainty increases bit by bit.