Robust optimization for integrated scheduling of sequential batch processes with dynamics

In this work, an integrated scheduling and dynamic optimization problem for sequential batch processes is proposed. We then address the issue of uncertainty in sequential batch processes using a robust counterpart approach. When uncertainty occurs, which is inevitable in industrial processes, the robust solution guarantees feasibility while the deterministic solution may lead to significant drop in the profit or even infeasibility. A robust optimization approach is applied to deal with the uncertain coefficients. This robust formulation has almost the same size as the deterministic problem. Moreover, it maintains the linearity and is able to control the degree of the conservatism for the solution. The results show that when uncertainty occurs, the robust process strategy results in better profit than the deterministic process strategy and the robust approach has almost the same computational performance as the deterministic approach.