Robust Satisficing
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AbstractWe present a general framework for robust satisficing that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. The satisficing decision maker specifies an acceptable target, or loss of optimality compared with the empirical optimization model, as a trade-off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robust satisficing, termed as the fragility measure, and present its representation theorem. Focusing on Wasserstein distance measure, we present tractable robust satisficing models for risk-based linear optimization, combinatorial optimization, and linear optimization problems with recourse. Serendipitously, the insights to the approximation of the linear optimization problems with recourse also provide a recipe for approximating solutions for hard stochastic optimization problems without relatively complete recourse. We perform numerical studies on a portfolio optimization problem and a network lot-sizing problem. We show that the solutions to the robust satisficing models are more effective in improving the out-of-sample performance evaluated on a variety of metrics, hence alleviating the optimizer’s curse.
Acceptance Date14/10/2021
All Author(s) ListDaniel Zhouyu Long, Melvyn Sim, Minglong Zhou
Journal nameOperations Research
Year2023
Month1
Volume Number71
Issue Number1
Pages61 - 82
ISSN0030-364X
eISSN1526-5463
LanguagesEnglish-United States
Keywordsrobust optimization, robust satisficing, data-driven, discrete optimization, stochastic optimization, fragility measure

Last updated on 2024-16-10 at 14:11