边收集数据边排序选择，高效利用实时输入信息。
Ranking and Selection with Simultaneous Input Data Collection
- 同步采集多源数据流，动态分配资源
- 通过时间累积的模拟输出估计性能表现
- 适合需要实时数据决策的仿真优化场景
本文提出一种全新的排名与选择框架，适用于存在流式输入数据的情形。多条数据流的采集可能消耗不同类型资源，因此可同时进行。为利用流式输入数据，我们聚合不同时刻下由异构输入分布产生的模拟输出，形成性能评估估计量。通过刻画该估计量的渐近行为，构建了两个优化问题，以最优分配数据采集与模拟运行的预算。进一步设计了一种多阶段并行预算分配方法，并提供了其统计性质，包括一致性与渐近正态性。通过若干数值实验验证了所提方法的优越性能。
原文摘要 · Abstract (English)
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 characterizing 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.
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