Department of Industrial Engineering & Decision Analytics [Joint IEDA/ISOM seminar] - Optimizing the Input Data Collection for Ranking and Selection

10:30am - 11:30am
** Room 4503 ** [lift 25-26]

We investigate a Bayesian ranking and selection (R&S) problem under input uncertainty when all solutions share a collection of independent input models. These Bayesian input models are updated as more data is collected from multiple independent sources ultimately converging to the data-generating distributions. Our goal is to design a sequential sampling algorithm that identifies the true optimum under the data-generating distributions most efficiently given a data collection budget. Two types of data collections are considered: input data acquisition from each source and simulation sampling for a solution at a particular set of input models. We propose the most probable best (MPB) as a Bayesian estimator of the optimum and characterize its probabilistic convergence rate to the optimum focusing on the case when there is a finite set of candidates for the true input distributions. We derive the optimal asymptotic static sampling ratios for the input data collection and simulation by maximizing the rate function. Based on this analysis, a sequential sampling algorithm is proposed, which is further bolstered by combining the kernel ridge regression to improve the mean prediction at the solution-input model pairs. We benchmark our algorithm against a state-of-the-art method that considers the same R&S problem.

Event Format
Speakers / Performers:
Prof. Eunhye Song
Georgia Institute of Technology, H. Milton Stewart School of Industrial and Systems Engineering

Eunhye Song is a Coca-Cola Foundation Early Career Professor and Assistant Professor at the H. Milton Stewart School of Industrial and Systems Engineering. She earned her PhD in Industrial Engineering and Management Sciences from Northwestern University in 2017. She was a Harold and Inge Marcus Early Career Assistant Professor in Industrial and Manufacturing Engineering at Penn State University from 2017 to 2022 before joining Georgia Tech in July 2022. Her core research area is theoretical and algorithmic analysis of stochastic simulation with a focus on robust decision-making with data-driven simulation. She is a recipient of the US National Science Foundation CAREER Award and won an Honorable Mention in the 2020 INFORMS JFIG Paper Competition. She is an active member of the INFORMS Simulation Society and has served on various conference organizing/award committees since 2017.

Recommended For
Faculty and staff
PG students
Department of Industrial Engineering & Decision Analytics
Department of Information Systems, Business Statistics & Operations Management
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