The Costs Optimization Model Based on Stochastic Model Predictive Control in Multi-level Supply Chain

Abstract

The multi-level supply chain consists on different levels of interveners containing warehouses, distribution centers, retailers, etc. In fact, it is reciprocal relationship between storage costs and transportation costs. The problem is how to optimize the combined costs of the storage and the transportation of the products across all the nodes of the supply chain. Model predictive control (MPC) is an advanced method of process control that has been in use in the complex dynamical systems like chemical process plant and supply chain. The MPC is usually used in supply chain management, under constraints like buffer limits and shipping capacities limits, based on approximations which make the future values of disturbance predicted, thus no recourse is available in the future. However, most real life applications are not only subject to constraints but also involve stochastic uncertainty. This paper proposes a costs optimization model use a stochastic model predictive control to optimize the combined costs of storage and shipping in a multi-level supply chain, to take into account the stochastic demand. Lastly, the costs optimization model’s simulation work is done in MATLAB, the experiment results show that the costs optimization model performs better than the naïve and the greedy control and the Model Predictive Control.