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arXiv · 2407.12100

An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance

Mohammadmahdi Ghasemloo·David J. Eckman
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Abstract

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.

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Mohammadmahdi Ghasemloo, David J. Eckman. 2024-07-16. An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance. https://doi.org/10.1287/ijds.2024.0056

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