.png){kind=icon}
Return
Two-Stage Robust Optimization Under Decision Dependent Uncertainty
.png){kind=icon}
PRE
AI
DOI：10.1109/JAS.2022.105512.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Benders decomposition
decision-dependent uncertainty
endogenous uncertainty
robust optimization (RO)
Journal
I
IF:
19.2
Papers:
1.4K
Citations:
1.1W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave View more