An Optimal Scheduling Model for Robust Management of Supply Chain

Supply chain management (SCM) aims to integrate plants with their suppliers and customers with materials flows so that they can be managed as a single entity. Traditional approaches available in the SCM literature usually focus on the optimization of each business node and neglect the dynamic interaction among them. An information sharing based model is developed that coordinate the network of business relationships among its nodes by integrating both discrete and continuous time metrics. The optimization models include the physical constraints while maintaining a certain degree of robustness against uncertainty in demand. The result generated by the supply chain model is optimized through model predictive control (MPC). Optimal production schedules, inventories levels, and material flow rates are simulated in a case study and compared for traditional distributed schemes. It is shown that the optimal scheduling results in an improvement of the bullwhip effect, customer satisfaction, and the total profit.