Güzin Bayraksan
Zitiert von
Zitiert von
Monte Carlo sampling-based methods for stochastic optimization
T Homem-de-Mello, G Bayraksan
Surveys in Operations Research and Management Science 19 (1), 56-85, 2014
Data-driven stochastic programming using phi-divergences
G Bayraksan, DK Love
The operations research revolution, 1-19, 2015
Assessing solution quality in stochastic programs
G Bayraksan, DP Morton
Mathematical Programming 108, 495-514, 2006
Reliable water supply system design under uncertainty
G Chung, K Lansey, G Bayraksan
Environmental Modelling & Software 24 (4), 449-462, 2009
A sequential sampling procedure for stochastic programming
G Bayraksan, DP Morton
Operations Research 59 (4), 898-913, 2011
An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments
S Kucuksari, AM Khaleghi, M Hamidi, Y Zhang, F Szidarovszky, ...
Applied Energy 113, 1601-1613, 2014
Assessing solution quality in stochastic programs via sampling
G Bayraksan, DP Morton
Decision Technologies and Applications, 102-122, 2009
Identifying effective scenarios in distributionally robust stochastic programs with total variation distance
H Rahimian, G Bayraksan, T Homem-de-Mello
Mathematical Programming 173 (1-2), 393-430, 2019
Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach
B Keller, G Bayraksan
Iie Transactions 42 (1), 16-30, 2009
Phi-divergence constrained ambiguous stochastic programs for data-driven optimization
D Love, G Bayraksan
Technical report, Department of Integrated Systems Engineering, The Ohio …, 2015
Data-driven sample average approximation with covariate information
R Kannan, G Bayraksan, JR Luedtke
arXiv preprint arXiv:2207.13554, 2022
Reclaimed water distribution network design under temporal and spatial growth and demand uncertainties
W Zhang, G Chung, P Pierre-Louis, G Bayraksan, K Lansey
Environmental modelling & software 49, 103-117, 2013
Controlling risk and demand ambiguity in newsvendor models
H Rahimian, G Bayraksan, T Homem-de-Mello
European Journal of Operational Research 279 (3), 854-868, 2019
Simulation optimization: A panel on the state of the art in research and practice
MC Fu, G Bayraksan, SG Henderson, BL Nelson, WB Powell, IO Ryzhov, ...
Proceedings of the Winter Simulation Conference 2014, 3696-3706, 2014
Decomposition algorithms for risk-averse multistage stochastic programs with application to water allocation under uncertainty
W Zhang, H Rahimian, G Bayraksan
INFORMS Journal on Computing 28 (3), 385-404, 2016
Fixed-width sequential stopping rules for a class of stochastic programs
G Bayraksan, P Pierre-Louis
SIAM Journal on Optimization 22 (4), 1518-1548, 2012
Residuals-based distributionally robust optimization with covariate information
R Kannan, G Bayraksan, JR Luedtke
Mathematical Programming, 1-57, 2023
Stochastic constraints and variance reduction techniques
T Homem-de-Mello, G Bayraksan
Handbook of simulation optimization, 245-276, 2014
A combined deterministic and sampling-based sequential bounding method for stochastic programming
P Pierre-Louis, G Bayraksan, DP Morton
Proceedings of the 2011 winter simulation conference (Wsc), 4167-4178, 2011
A probability metrics approach for reducing the bias of optimality gap estimators in two-stage stochastic linear programming
R Stockbridge, G Bayraksan
Mathematical Programming 142, 107-131, 2013
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