Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling

Abstract

Distributionally robust optimization has garnered significant attention for its effectiveness in decision-making under uncertainty. However, employing this strategy faces hurdles posed by intractable models and the difficulty in parameter determination while tackling production scheduling issues under uncertainty. This work presents a novel data-driven distributionally robust optimization framework to address these challenges. A data-driven combined ambiguity set, which incorporates Wasserstein distance and moment information, is devised to yield less conservative solutions. Additionally, a data-driven support set established based on an improved kernel technique is introduced to help identify and exclude potential outliers. The relevant ambiguous parameters are determined through bi-level cross-validation. Subsequently, the data-driven distributionally robust optimization model under combined ambiguity is reformulated into tractable by dual theory. The application to industrial scheduling shows that the proposed method can effectively utilize data information and better hedge against uncertainties while obtaining higher profits.