Séminaire de recherche CAMS avec David Eckman (Texas A&M University)

Lieu : Campus Lille
Heure : 1hh-12h 
Intervenant : David Eckman (Texas A&M University) 

Résumé : Analyzing stochastic simulation outputs using statistical learning methods 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 introduce a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster multivariate empirical distributions produced by a limited offline simulation experiment. 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.

Biographie : David J. Eckman is an Assistant Professor in the Wm Michael Barnes '64 Department of Industrial and Systems Engineering at Texas A&M University. He received a Ph.D. in Operations Research from Cornell University and was a postdoctoral scholar in the Department of Industrial Engineering and Management Sciences at Northwestern University. His research interests deal with optimization and output analysis for stochastic simulation models. He is a co-creator of SimOpt, a testbed of simulation-optimization problems and solvers, and is currently the treasurer of the INFORMS Simulation Society.