Segui
Eunhye Song
Titolo
Citata da
Citata da
Anno
Shapley effects for global sensitivity analysis: Theory and computation
E Song, BL Nelson, J Staum
SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1060-1083, 2016
2132016
Advanced tutorial: Input uncertainty quantification
E Song, BL Nelson, CD Pegden
Proceedings of the Winter Simulation Conference 2014, 162-176, 2014
1052014
Quickly assessing contributions to input uncertainty
E Song, BL Nelson
IIE Transactions 47 (9), 893-909, 2015
742015
Gaussian Markov random fields for discrete optimization via simulation: Framework and algorithms
P L. Salemi, E Song, BL Nelson, J Staum
Operations Research 67 (1), 250-266, 2019
632019
Single-experiment input uncertainty
Y Lin, E Song, BL Nelson
Journal of Simulation 9 (3), 249-259, 2015
492015
Input–output uncertainty comparisons for discrete optimization via simulation
E Song, BL Nelson
Operations Research 67 (2), 562-576, 2019
462019
Input uncertainty and indifference-zone ranking & selection
E Song, BL Nelson, LJ Hong
2015 Winter Simulation Conference (WSC), 414-424, 2015
322015
A quicker assessment of input uncertainty
E Song, BL Nelson
2013 Winter simulations conference (WSC), 474-485, 2013
252013
Rapid discrete optimization via simulation with Gaussian Markov random fields
M Semelhago, BL Nelson, E Song, A Wchter
INFORMS Journal on Computing 33 (3), 915-930, 2021
232021
Input uncertainty in stochastic simulation
RR Barton, H Lam, E Song
The Palgrave Handbook of Operations Research, 573-620, 2022
202022
Online improvement of condition-based maintenance policy via Monte Carlo tree search
M Hoffman, E Song, MP Brundage, S Kumara
IEEE Transactions on Automation Science and Engineering 19 (3), 2540-2551, 2021
172021
Revisiting direct bootstrap resampling for input model uncertainty
RR Barton, H Lam, E Song
2018 Winter Simulation Conference (WSC), 1635-1645, 2018
162018
Condition-based maintenance policy optimization using genetic algorithms and Gaussian Markov improvement algorithm
ML Hoffman
132019
Stochastic approximation for simulation optimization under input uncertainty with streaming data
E Song, UV Shanbhag
2019 Winter Simulation Conference (WSC), 3597-3608, 2019
122019
Efficient input uncertainty quantification via green simulation using sample path likelihood ratios
BM Feng, E Song
2019 Winter Simulation Conference (WSC), 3693-3704, 2019
112019
Input model risk
E Song, BL Nelson
Advances in Modeling and Simulation: Seminal Research from 50 Years of…, 2017
112017
Event graph modeling of a homogeneous job shop with bi-inline cells
E Song, BK Choi, B Park
Simulation Modelling Practice and Theory 20 (1), 1-11, 2012
112012
Computational methods for optimization via simulation using gaussian markov random fields
M Semelhago, BL Nelson, A Wchter, E Song
2017 Winter Simulation Conference (WSC), 2080-2091, 2017
92017
Input-output uncertainty comparisons for optimization via simulation
E Song
2016 Winter Simulation Conference (WSC), 3666-3667, 2016
82016
Selection of the most probable best under input uncertainty
KK Kim, T Kim, E Song
2021 Winter Simulation Conference (WSC), 1-12, 2021
62021
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
Articoli 1–20