Bae, K-H
; Feng, B
; Kim, S
; Lazarova-Molnar, S
; Zheng, Z
; Roeder, T
; Thiesing, R
(Ed.)
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.
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