Adjustable Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets
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AbstractWe study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems.
All Author(s) ListRuan Haolin , Chen Zhi , Ho Pang Chin
Journal nameINFORMS Journal on Computing
Year2023
Month9
Volume Number35
Issue Number5
PublisherINFORMS Inst.for Operations Res.and the Management Sciences
Pages1002 - 1023
ISSN1091-9856
eISSN1526-5528
LanguagesEnglish-United States