Process Scheduling under Ambiguity Uncertainty Probability Distribution

Abstract Distributionally robust optimization (DRO) has been gaining increasing popularity in decision-making under uncertainties due to its capability in handling ambiguity of distributions. However, it is a nontrivial task to approach the scheduling problem within the DRO framework. In this paper, we propose a novel DRO scheduling model under uncertain demand. We adopt max-out moment functions to characterize the ambiguity set, which includes a variety of distributions. In addition, we take into account recourse decisions made after random demands become known. In this way, a two-stage distributionally robust scheduling model can be developed, which can be approximately solved by linear decision rules. Applications in industrial-scale batch process scheduling show that, the proposed approach can leverage empirical data information with effect, better hedge against the inexactness of uncertainty distributions, and bring more profits.