Using statistical learning methods to analyze stochastic simulation outputs can significantly enhance decision-making by uncovering relationships between different simulated systems and between a system's inputs and outputs. We focus on clustering multivariate empirical distributions of simulation outputs to identify patterns and trade-offs among performance measures. We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster these multivariate empirical distributions. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring. In numerical experiments involving a call-center model, we demonstrate how this methodology can identify staffing plans that yield similar performance outcomes and inform policies for intervening when queue lengths signal potentially worsening system performance.

翻译：运用统计学习方法分析随机仿真输出，能够通过揭示不同仿真系统之间以及系统输入与输出之间的关系，显著提升决策质量。本文聚焦于对仿真输出的多元经验分布进行聚类，以识别性能指标间的模式与权衡关系。我们提出一种新颖的聚合聚类算法，该算法利用正则化Wasserstein距离对多元经验分布进行聚类。该框架具有若干重要应用场景，包括异常检测、预优化与在线监测。在涉及呼叫中心模型的数值实验中，我们展示了该方法如何识别产生相似性能结果的人员配置方案，并为队列长度预示系统性能可能恶化时的干预策略提供依据。