Abstract
Classical ranking-and-selection (R&S) procedures cannot be applied directly to select the best decision in the presence of distributional ambiguity. In this paper we propose a robust selection of the best (RSB) framework which compares decisions based on their worst-case performances over a finite set of possible distributions and selects the decision with the best worst-case performance. To solve the RSB problems, we design a two-layer R&S procedure under the indifference-zone formulation. The procedure identifies the worst-case distribution in the first stage and the best decision in the second. We prove the statistical validity of the two-layer procedure and test its performance numerically.
Xiaowei Zhang
Associate Professor
My research research focuses on methodological advances in stochastic simulation and optimization, decision analytics, and reinforcement learning, with applications in service operations management, financial technology, and digital economy.