Data-driven robust optimization for pipeline scheduling under flow rate uncertainty

• Published in: Computers & chemical engineering Vol. 193; p. 108924
• Main Authors: Baghban, Amir, Castro, Pedro M., Oliveira, Fabricio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Continuous-time formulation; Mixed-integer linear programming; Robust optimization; Straight liquid pipelines; Support vector clustering; Straight liquid pipelines; Continuous-time formulation; Robust optimization; Support vector clustering; Mixed-integer linear programming
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2024.108924

•Efficient deterministic continuous-time MILP model for scheduling the transportation of oil derivatives via pipeline.•Smaller-sized model and faster computational time compared to previous models.•Scheduling under uncertainty using data-driven robust optimization.•Application of support vector clustering in forming the uncertainty set.•Easy and meaningful trade-off between robustness and optimality. Frequently, parameters in optimization models are subject to a high level of uncertainty coming from several sources and, as such, assuming them to be deterministic can lead to solutions that are infeasible in practice. Robust optimization is a computationally efficient approach that generates solutions that are feasible for realizations of uncertain parameters near the nominal value. This paper develops a data-driven robust optimization approach for the scheduling of a straight pipeline connecting a single refinery with multiple distribution centers, considering uncertainty in the injection rate. For that, we apply support vector clustering to learn an uncertainty set for the robust version of the deterministic model. We compare the performance of our proposed robust model against one utilizing a standard robust optimization approach and conclude that data-driven robust solutions are less conservative.