Robust Selection of the Best
Dec 10, 2013·
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0 min read
Weiwei Fan
L. Jeff Hong
Xiaowei Zhang
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.
Type
Publication
Proceedings of the 2013 Winter Simulation Conference, 713–723
Authors
Professor in Department of Industrial and Systems Engineering at the University of Minnesota.
Authors
I am an Associate Professor at HKUST, jointly appointed in the Department of Industrial Engineering and Decision Analytics and the Department of Economics, and the Academic Director of the MSc in FinTech program. I serve as an Associate Editor for several leading journals in the field, including Management Science, Operations Research, Navel Research Logistics, and Queueing Systems.