| 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 | 390 | 2014 |
| Data-driven stochastic programming using phi-divergences G Bayraksan, DK Love The operations research revolution, 1-19, 2015 | 280 | 2015 |
| Assessing solution quality in stochastic programs G Bayraksan, DP Morton Mathematical Programming 108, 495-514, 2006 | 210 | 2006 |
| Reliable water supply system design under uncertainty G Chung, K Lansey, G Bayraksan Environmental Modelling & Software 24 (4), 449-462, 2009 | 156 | 2009 |
| A sequential sampling procedure for stochastic programming G Bayraksan, DP Morton Operations Research 59 (4), 898-913, 2011 | 152 | 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 | 126 | 2014 |
| Identifying effective scenarios in distributionally robust stochastic programs with total variation distance H Rahimian, G Bayraksan, T Homem-de-Mello Mathematical Programming 173 (1), 393-430, 2019 | 94 | 2019 |
| Assessing solution quality in stochastic programs via sampling G Bayraksan, DP Morton Decision Technologies and Applications, 102-122, 2009 | 80 | 2009 |
| Data-driven sample average approximation with covariate information R Kannan, G Bayraksan, JR Luedtke Operations Research, 2025 | 65 | 2025 |
| Scheduling jobs sharing multiple resources under uncertainty: A stochastic programming approach B Keller, G Bayraksan Iie Transactions 42 (1), 16-30, 2009 | 63 | 2009 |
| Residuals-based distributionally robust optimization with covariate information R Kannan, G Bayraksan, JR Luedtke Mathematical Programming 207 (1), 369-425, 2024 | 57 | 2024 |
| 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 | 57* | 2019 |
| Phi-divergence constrained ambiguous stochastic programs for data-driven optimization D Love, G Bayraksan Technical report, Department of Integrated Systems Engineering, The Ohio …, 2015 | 50* | 2015 |
| 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 | 43 | 2016 |
| 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 | 43 | 2013 |
| 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 | 39 | 2014 |
| Love. Data-driven stochastic programming using phi-divergences G Bayraksan, K David The operations research revolution, 1-19, 0 | 37 | |
| 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 | 36 | 2012 |
| A multistage distributionally robust optimization approach to water allocation under climate uncertainty J Park, G Bayraksan European Journal of Operational Research 306 (2), 849-871, 2023 | 28 | 2023 |
| Stochastic constraints and variance reduction techniques T Homem-de-Mello, G Bayraksan Handbook of simulation optimization, 245-276, 2014 | 27 | 2014 |
Tito Homem-de-MelloProfessor. School of Business. Universidad Adolfo Ibañez.Verifierad e-postadress på uai.cl
