Publications

• Q. Zhu, X. Yu, and G. Bayraksan, Residuals-Based Contextual Distributionally Robust Optimization with Decision-Dependent Uncertainty
• R. Kannan, G. Bayraksan and J.R. Luedtke, Heteroscedasticity-aware residuals-based contextual stochastic optimization
• R. Kannan, G. Bayraksan and J.R. Luedtke, Data-Driven Sample Average Approximation with Covariate Information (recorded presentation (30-min))
• Love, D.K.* and G. Bayraksan, “Phi-divergence Constrained Ambiguous Stochastic Programs for Data-Driven Optimization”    [e-print]

• Yurdakul, O and G. Bayraksan, A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals, IEEE Transactions on Power Systems, forthcoming, 2024
• R. Kannan, G. Bayraksan and J.R. Luedtke, Residuals-Based Distributionally Robust Optimization with Covariate Information, Mathematical Programming, 207: 369–425, 2024
• Bayraksan, G. F. Maggioni, D. Faccini and M. Yang*, Bounds for Multistage Mixed-Integer Distributionally Robust Optimization, SIAM Journal on Optimization, 34(1): 682-717, 2024
• M. Yang* and G. Bayraksan, Stochastic Multistage Multiobjective Water Allocation with Hedging Rule, Journal of Water Resources Planning and Management, 149(9): 04023047, 1-16, 2023
• Park, J.*, and G. Bayraksan, A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty, European Journal of Operational Research, 306(2): 849-871, 2023
• Rahimian, H.*, G. Bayraksan and T. Homem-de-Mello, Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance, SIAM Journal on Optimization, 32(3): 1698–1727, 2022
• Park, J.*, R. Stockbridge* and G. Bayraksan, Variance Reduction in Sequential Sampling for Stochastic Programming, Annals of Operations Research, 300(1): 171–204, 2021
• Rahimian, H.*, G. Bayraksan and T. Homem-de-Mello, Controlling Risk and Demand Ambiguity in Newsvendor Models, European Journal of Operational Research, 279(3): 854–868, 2019
• Rahimian, H.*, G. Bayraksan and T. Homem-de-Mello, Identifying Effective Scenarios in Distributionally Robust Stochastic Programs with Variation Distance, Mathematical Programming, 173(1–2): 393–430, 2019

  (Runner-Up, ICS Student Paper Prize)

• Bayraksan, G., An Improved Averaged Two-Replication Procedure with Latin Hypercube Sampling, Operations Research Letters, 46(2): 173-178, 2018
• Zhang, W.*, H. Rahimian* and G. Bayraksan, “Decomposition Algorithms for Risk-Averse Multistage Stochastic Programs with Application to Water Allocation under Uncertainty,” INFORMS Journal on Computing, 28(3): 385-404, 2016
• Stockbridge, R.* and G. Bayraksan, “Variance Reduction in Monte Carlo Sampling-Based Optimality Gap Estimators for Two-Stage Stochastic Linear Programming,” Computational Optimization and Applications, 64(2):407-43, 2016
• Lan, F.*, G. Bayraksan and K. Lansey, “Reformulation Linearization Technique based Branch-and-Reduce Approach to the Regional Water Supply System Design Problem,” Engineering Optimization, 48(3): 454-475, 2016
• Bayraksan, G. and D. Love*, “Data-Driven Stochastic Programming using Phi-Divergences,” TutORials in Operations Research, INFORMS, Hanover, MD, 1–19, 2015
• Love, D.* and G. Bayraksan, “Overlapping Batches for the Assessment of Solution Quality in Stochastic Programs,” ACM Transactions on Modeling and Computer Simulation (TOMACS), 25(3), 2015
• Homem-de-Mello, T. and G. Bayraksan, “Monte Carlo Sampling-Based Methods for Stochastic Optimization,” Surveys in Operations Research and Management Science, 19(1): 56–85, 2014
• Küçüksarı, S., A.M. Khaleghi, M. Hamidi*, Y. Zhang*, F. Szidarovszky, G. Bayraksan, Y.-J. Son “A GIS, Optimization, and Simulation Framework for Optimal PV Size and Location in Campus Area Environments,” Applied Energy, 113:1601–1613, 2014
• Stockbridge, R.* and G. Bayraksan, “A Probability Metrics Approach for Reducing the Bias of Optimality Gap Estimators in Two-Stage Stochastic Linear Programming,” Mathematical Programming, 147:107–131, 2013  
• Zhang, W.*, G. Chung, P. Pierre-Louis*, G. Bayraksan and K. Lansey, “Reclaimed Water Network Design under Temporal and Spatial Growth and Demand Uncertainties,” Environmental Modelling & Software, 49, 103–117, 2013  (Awarded INFORMS ENRE Best Publication Award in Environment and Sustainability)   [paper]

• Bayraksan, G. and P. Pierre-Louis*, “Fixed-Width Sequential Stopping Rules for a Class of Stochastic Programs,” SIAM Journal on Optimization, 22(4), 1518–1548, 2012       [paper]
• Keller, B.* and G. Bayraksan, “Disjunctive Decomposition for Two-Stage Stochastic Integer Programs with GUB Constraints,” INFORMS Journal on Computing, 24, 172–186, 2012    [paper]
• Keller, B.* and G. Bayraksan, “Quantifying Operational Risk in Financial Institutions,’’ INFORMS Transactions on Education, 12(2), 100–113, 2012    (This has won the INFORMS Best Case Study Award)   [www]
• Bayraksan, G. and D. P. Morton, “A Sequential Sampling Procedure for Stochastic Programming,” Operations Research, 59(4), 898–913, 2011     [paper]
• Keller, B.* and G. Bayraksan, “Scheduling Jobs Sharing Multiple Resources under Uncertainty: A Stochastic Programming Approach,” IIE Transactions, 42, 16–30, 2010    [paper]
• Bayraksan, G. and D.P. Morton, “Assessing Solution Quality via Sampling in Stochastic Programs,” TutORials in Operations Research, Volume 5, 102–122, INFORMS, Hannover, MD, 2009 [paper]
• Chung, G.*, Lansey, K. and G. Bayraksan, “Reliable Water Supply System Design under Uncertainty,” Environmental Modeling & Software, 24, 449–462, 2009  
• Bayraksan, G. and D. P. Morton, “Assessing Solution Quality in Stochastic Programs,” Mathematical Programming, 108,  495–514, 2006   [paper]

Book Chapters

• Homem-de-Mello, T. and G. Bayraksan, “Stochastic Constraints and Variance Reduction Techniques,” Handbook of Simulation Optimization, edited by Michael C. Fu, pp. 245–276, 2015
• Bayraksan, G., “Solving Stochastic Programs,” Wiley Encyclopedia of Operations Research and Management Science, 2011
• Bayraksan, G., D. P. Morton and A. Partani, “Simulation-Based Optimality Tests for Stochastic Programs,” Stochastic Programming: The State of the Art, In Honor of George B. Dantzig, G. Infanger (ed), International Series in Operations Research & Management Science, Vol. 150, Springer, New York, 37–55, 2011    [preprint]

• M.C. Fu, G. Bayraksan, S.G. Henderson, B.L. Nelson, W.B. Powell, I.O. Ryzhov, B. Thengvall, “ Simulation Optimization: A Panel on the State of the Art in Research and Practice,” Proceedings of the 2014 Winter Simulation Conference (WSC), 3696-3706, 2014
• Love, D. and G. Bayraksan, “Two-Stage Likelihood Robust Linear Program with Application to Water Allocation under Uncertainty,” Proceedings of the 2013 Winter Simulation Conference (WSC), edited by R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M.E. Kuhl, 2013   [paper]
• Love, D. and G. Bayraksan, “Overlapping Batches for Assessing Solution Quality in Stochastic Programs,’’ Proceedings of the 2011 Winter Simulation Conference (WSC), edited by Jain et al., 4184–4195, 2011
• Pierre-Louis, P, D.P. Morton and G. Bayraksan, “A Combined Deterministic and Sampling-Based Sequential Bounding Method for Stochastic Programming,” Proceedings of the 2011 Winter Simulation Conference (WSC), edited by Jain et al., 4172–4183, 2011    [www]
• Zhang, W., G. Bayraksan, G. Chung and K. Lansey, “Optimal Reclaimed Water Network Design via Two-Stage Stochastic Binary Programming,” Proceedings of the 2010 Water Distribution System Analysis Conference (WDSA 2010), ASCE, 2010    [www]
• Bayraksan, G. and D. P. Morton, “Sequential Sampling for Stochastic Programming,” Proceedings of the 2007 Winter Simulation Conference, 421–429, 2007
• Morton, D. P. and G. Bayraksan, “On Solution Quality in Stochastic Programming,” in Algorithms for Optimization with Incomplete Information, Dagstuhl Seminar, Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany, 2005
• Bayraksan, G. and D. P. Morton, “Testing Solution Quality in Stochastic Programming: A Single Replication Procedure,” Proceedings of the 16th Symposium of IASC on Computational Statistics, Physica-Verlag/Springer, Prague, Czech Republic, 2004