G
Grani A. Hanasusanto
Researcher at University of Texas at Austin
Publications - 40
Citations - 1111
Grani A. Hanasusanto is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Robust optimization & Semidefinite programming. The author has an hindex of 10, co-authored 36 publications receiving 788 citations. Previous affiliations of Grani A. Hanasusanto include École Polytechnique Fédérale de Lausanne & Imperial College London.
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K-Adaptability in Two-Stage Robust Binary Programming
TL;DR: This paper approximate two-stage robust binary programs by their corresponding K -adaptability problems, in which the decision maker precommits to K second-stage policies, here -and-now, and implements the best of these policies once the uncertain parameters are observed.
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A distributionally robust perspective on uncertainty quantification and chance constrained programming
TL;DR: The watershed between tractability and intractability in ambiguity-averse uncertainty quantification and chance constrained programming is delineated and tools from distributionally robust optimization are derived that derive explicit conic reformulations for tractable problem classes and suggest efficiently computable conservative approximations for intractable ones.
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Ambiguous Joint Chance Constraints Under Mean and Dispersion Information
TL;DR: It is demonstrated that the pessimistic joint chance constraints are conic representable if (i) the constraint coefficients of the decisions are deterministic, (ii) the support set of the uncertain parameters is a cone, and (iii) the dispersion function is of first order, that is, it is positively homogeneous.
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Distributionally robust multi-item newsvendor problems with multimodal demand distributions
TL;DR: This work presents a risk-averse multi-dimensional newsvendor model for a class of products whose demands are strongly correlated and subject to fashion trends that are not fully understood at the time when orders are placed and demonstrates that disregarding ambiguity or multimodality can lead to unstable solutions that perform poorly in stress test experiments.
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Data-driven inverse optimization with imperfect information
TL;DR: This paper formalizes this inverse optimization problem as a distributionally robust program minimizing the worst-case risk that the predicted decision differs from the agent’s actual response to a random signal and shows that the emerging inverse optimization problems can be exactly reformulated as tractable convex programs when a new suboptimality loss function is used.