Data‐Driven Ranking and Selection With Simultaneous Input Data Collection and Simulation
提出一种新方法，在存在流式输入数据时进行排序与选择，通过聚合不同输入分布下的仿真输出估计性能，并优化数据收集与仿真预算分配，提供统计保证。
ABSTRACT In this paper, we propose a general and novel formulation of ranking and selection with the existence of streaming input data. The collection of multiple streams of such data may consume different types of resources, and hence can be conducted simultaneously. To utilize the streaming input data, we aggregate simulation outputs generated under heterogeneous input distributions over time to form a performance estimator. By ch aracterizing the asymptotic behavior of the performance estimators, we formulate two optimization problems to optimally allocate budgets for collecting input data and running simulations. We then develop a multi‐stage simultaneous budget allocation procedure and provide its statistical guarantees, such as consistency and asymptotic normality. We conduct several numerical studies to demonstrate the competitive performance of the proposed procedure.