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- Title
Data-Driven Ranking and Selection Under Input Uncertainty.
- Authors
Wu, Di; Wang, Yuhao; Zhou, Enlu
- Abstract
In many applications, input data are collected frequently to update the simulation model of the system, whereas simulation is run to compare different designs/strategies to identify the best one with a high confidence. In "Data-Driven Ranking and Selection Under Input Uncertainty," Wu, Wang, and Zhou consider such a simulation-based ranking and selection (R&S) problem, in which the input distribution is estimated and updated with input data arriving in batches over time. Unlike most existing works of R&S that conduct simulation under a fixed distribution, in this data-driven setting, simulation outputs are generated under different input distributions over time. A moving average estimator is introduced to aggregate simulation outputs generated under heterogenous distributions. Then, two sequential elimination procedures are devised by establishing exact and asymptotic confidence bands for the estimator. The efficiency of the procedures can be further boosted by incorporating the "indifference zone" idea and optimizing the "drop rate" parameter of the moving average estimator. We consider a simulation-based ranking and selection (R&S) problem with input uncertainty, in which unknown input distributions can be estimated using input data arriving in batches of varying sizes over time. Each time a batch arrives, additional simulations can be run using updated input distribution estimates. The goal is to confidently identify the best design after collecting as few batches as possible. We first introduce a moving average estimator for aggregating simulation outputs generated under heterogenous input distributions. Then, based on a sequential elimination framework, we devise two major R&S procedures by establishing exact and asymptotic confidence bands for the estimator. We also extend our procedures to the indifference zone setting, which helps save simulation effort for practical usage. Numerical results show the effectiveness and necessity of our procedures in controlling error from input uncertainty. Moreover, the efficiency can be further boosted through optimizing the "drop rate" parameter, which is the proportion of past simulation outputs to discard, of the moving average estimator. Funding: The authors gratefully acknowledge support by the National Science Foundation Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-1453934] and Division of Mathematical Sciences [Grant DMS2053489] and the Air Force Office of Scientific Research [Grants FA9550-19-1-0283 and FA9550-22-1-0244]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2022.2375.
- Subjects
NATIONAL Science Foundation (U.S.); UNITED States Air Force Academy; MOVING average process; ELECTRONIC materials; AIR forces; APATHY
- Publication
Operations Research, 2024, Vol 72, Issue 2, p781
- ISSN
0030-364X
- Publication type
Article
- DOI
10.1287/opre.2022.2375