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Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance

Hamed Rahimian (hrahimi***at***clemson.edu)
Guzin Bayraksan (bayraksan.1***at***osu.edu)
Tito Homem-de-Mello (tito.hmello***at***uai.cl)

Abstract: We study multistage distributionally robust optimization (DRO) to hedge against ambiguity in quantifying the underlying uncertainty of a problem. Recognizing that not all the realizations and scenario paths might have an "effect" on the optimal value, we investigate the question of how to define and identify critical scenarios for nested multistage DRO problems. Our analysis extends the work of Rahimian, Bayraksan, and Homem-de-Mello [Math. Program. 173(1--2): 393--430, 2019], which was in the context of a static/two-stage setting, to the multistage setting. To this end, we define the notions of effectiveness of scenario paths and the conditional effectiveness of realizations along a scenario path for a general class of multistage DRO problems. We then propose easy-to-check conditions to identify the effectiveness of scenario paths in the multistage setting when the distributional ambiguity is modeled via the total variation distance. Numerical results show that these notions provide useful insight on the underlying uncertainty of the problem.

Keywords: Multistage distributionally robust optimization, Effective scenarios, Total variation distance

Category 1: Stochastic Programming

Category 2: Robust Optimization

Citation:

Download: [PDF]

Entry Submitted: 09/13/2021
Entry Accepted: 09/14/2021
Entry Last Modified: 09/14/2021

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