An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance

• URL: http://arxiv.org/abs/2407.12100v2
• Date: Sun, 3 Nov 2024 05:34:36 GMT
• Title: An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance
• Authors: Mohammadmahdi Ghasemloo, David J. Eckman,
• Abstract summary: We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster simulation outputs. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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