Data-driven crude oil scheduling optimization with a distributionally robust joint chance constraint under multiple uncertainties

Abstract

Crude oil scheduling optimization is crucial for decreasing the production cost of refineries. However, the feasibility of the optimized schemes is challenged by uncertainties such as possible ship arrival delays and fluctuating crude demands. This study develops a novel data-driven continuous-time optimization model with a distributionally robust joint chance constraint to ensure the overall feasibility probability of the crude oil processing plan under multiple uncertainties. Industrial data are collected to build an ambiguity set using Wasserstein distance to include the potential joint probability distribution of uncertainties. The radius of the ambiguity set is chosen by cross-validation. The model is constructed considering the worst case in the ambiguity set. First, it is formulated as a conditional value-at-risk constrained optimization model. A big-M coefficient and additional binary variables are then utilized to convert the proposed model into a resolvable problem. The efficacy and reliability of the method are explored through case studies.