Adjustable Robust Optimization for the Multi-period Planning Operations of an Integrated Refinery-Petrochemical Site under Uncertainty

Abstract

This paper concentrates on the formulation of a large-scale nonconvex mixed-integer nonlinear programming model and the application of robust optimization for the multi-period operational planning of real-world integrated refinery-petrochemical site in China under uncertain product demands and crude oil price. To avoid excessive conservativeness resulting from classical static robust optimization, an adjustable robust counterpart incorporating resource decisions via an affinely adjustable linear decision rule is first derived. On the basis of a proposed polyhedral dynamic uncertainty set that mimics the dynamic behavior of the product demand over time, an adjustable robust counterpart with a dynamic uncertainty set is further formulated. Classical static robust optimization, adjustable robust optimization, and adjustable robust optimization with dynamic uncertainty sets are systematically compared for case studies. The results clearly illustrate the advantages of the affinely adjustable robust optimization with a dynamic uncertainty set over the classic robust optimization in decision making.