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Title: Metric Learning For Simulation Analytics
The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similarity between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.
Authors:
; ; ;
Editors:
Bae, K-H; Feng, B; Kim, S; Lazarova-Molnar, S; Zheng, Z; Roeder, T; Thiesing, R
Award ID(s):
1854562
Publication Date:
NSF-PAR ID:
10233323
Journal Name:
Proceedings of the Winter Simulation Conference
Page Range or eLocation-ID:
349-360
ISSN:
1558-4305
Sponsoring Org:
National Science Foundation
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