Steady-state quantile parameter estimation: An empirical comparison of stochastic kriging and quantile regression

Jennifer M. Bekki, Xi Chen, Demet Batur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


The time required to execute simulation models of modern production systems remains high even with todays computing power, particularly when what-if analyses need to be performed to investigate the impact of controllable system input variables on an output performance measure. Compared to mean and variance which are frequently used in practice, quantiles provide a more complete picture of the performance of the underlying system. Nevertheless, quantiles are more difficult to estimate efficiently through stochastic simulation. Stochastic kriging (SK) and quantile regression (QR) are two promising metamodeling tools for addressing this challenge. Both approximate the functional relationship between the quantile parameter of a random output (e.g., cycle time) and multiple input variables (e.g., start rate, unloading times). In this paper, we compare performances of SK and QR on steady-state quantile parameter estimation. Results are presented from simulations of an M/M/1 queue and a more realistic model of a semiconductor manufacturing system.

Original languageEnglish (US)
Title of host publicationProceedings of the 2014 Winter Simulation Conference, WSC 2014
EditorsAndreas Tolk, Levent Yilmaz, Saikou Y. Diallo, Ilya O. Ryzhov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781479974863
StatePublished - Jan 23 2015
Event2014 Winter Simulation Conference, WSC 2014 - Savannah, United States
Duration: Dec 7 2014Dec 10 2014

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2014 Winter Simulation Conference, WSC 2014
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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