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

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 (2019), pp. 393--430], 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.

Ketersediaan

Informasi Detail

Judul Seri

SIAM JOURNAL ON OPTIMIZATION; Vol.32 No.3 July-September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1698-1727

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TOTAL VARIATION DISTANCE
MULTISTAGE DISTRIBUTIONALLY ROBUST OPTIMIZATION
EFFECTIVE SCENARIOS

Info Detail Spesifik

https://doi.org/10.1137/21M1446484

Pernyataan Tanggungjawab

Hamed Rahimian, Guzin Bayraksan, Tito Homem De-Mello

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