Two-Stage Robust Optimization Under Decision Dependent Uncertainty

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Zhang, YF (Author) Other; Liu, F (Author) Other; Su, YF (Author) Other; Chen, Y (Author) Other; Wang, ZJ (Author) Other; Catalao, JPS (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: IEEE-CAA JOURNAL OF AUTOMATICA SINICA

Vol. 9

Pages: 1295-1306

ISSN: 2329-9266

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 13 Citations

Other information

Authenticus ID: P-00W-V8B

DOI: 10.1109/jas.2022.105512

Abstract (EN): In the conventional robust optimization (RO) context, the uncertainty is regarded as residing in a predetermined and fixed uncertainty set. In many applications, however, uncertainties are affected by decisions, making the current RO framework inapplicable. This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties. We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem. A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling. The computational tractability, robust feasibility and optimality, and convergence performance of the proposed algorithm are guaranteed with theoretical proof. Four motivating application examples that feature the decision-dependent uncertainties are provided. Finally, the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12