Two-Stage Robust Optimization Under Decision Dependent Uncertainty

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

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

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: IEEE-CAA JOURNAL OF AUTOMATICA SINICA

Vol. 9

Páginas: 1295-1306

ISSN: 2329-9266

Indexação

ISI Web of Knowledge - 10 Citações

Scopus - 13 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12