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- W4310251704 abstract "This paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second order stochastic dominance constrained problem (DRSSDCP) that hinges on using a type-1 Wasserstein ambiguity set. It is, furthermore, for the first time shown to be axiomatically motivated in an environment with distribution ambiguity. We formulate the DRSSDCP as a multistage robust optimization problem and further propose a tractable conservative approximation that exploits finite adaptability and a scenario-based lower bounding problem. We then propose the first exact optimization algorithm for this DRSSDCP. We illustrate how the data-driven DRSSDCP can be applied in practice on resource-allocation problems with both synthetic and real data. Our empirical results show that, with a proper adjustment of the size of the Wasserstein ball, DRSSDCP can reach acceptable out-of-sample feasibility yet still generating strictly better performance than what is achieved by the reference strategy." @default.
- W4310251704 created "2022-11-30" @default.
- W4310251704 creator A5014509810 @default.
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- W4310251704 date "2022-11-22" @default.
- W4310251704 modified "2023-09-28" @default.
- W4310251704 title "Data-Driven Optimization with Distributionally Robust Second Order Stochastic Dominance Constraints" @default.
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- W4310251704 doi "https://doi.org/10.1287/opre.2022.2387" @default.
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