Pro-active Scheduling by a Combined Robust Optimization and Multi-parametric Programming Approach

In this work, we address short-term batch process scheduling under uncertainty in which the scheduling model is contaminated with uncertain data in the objective function, the right-hand side vector and in the constraint matrix, introduced by price, demand, and processing time or conversion rate uncertainty, respectively. We apply a two-stage robust optimization/multi-parametric programming procedure for the approximate solution of the scheduling model which translates into a multi-parametric mixed integer linear (mp-MILP) problem. We demonstrate that the proposed approach contributes to the construction of a pro-active scheduling strategy and show that it is an attractive alternative to the rigorous robust optimization approach in terms of providing a tight estimate of the optimal scheduling policy.