Data-Driven Ranking and Selection Under Input Uncertainty
研究了数据驱动环境下仿真模型的排序与选择问题，提出移动平均估计量聚合异构分布下的仿真输出，并设计两种序贯淘汰程序，通过置信区间和参数优化提升效率。
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.