Publications by Topic

 

Contextual Stochastic Optimization (Integrated Learning and Optimization under Uncertainty)

 

• Q. Zhu, X. Yu, and G. Bayraksan, Residuals-Based Contextual Distributionally Robust Optimization with Decision-Dependent Uncertainty, submitted
• Yurdakul, O and G. Bayraksan, A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals, IEEE Transactions on Power Systems, 39(6): 6914-6933, 2024     [second part of this presentation, starting at 38:50]
• R. Kannan, G. Bayraksan and J.R. Luedtke, Data-Driven Sample Average Approximation with Covariate Information, Operations Research, Forthcoming, 2024     [first part of this presentation, until 38:50]
• R. Kannan, G. Bayraksan and J.R. Luedtke, Residuals-Based Distributionally Robust Optimization with Covariate Information, Mathematical Programming, 207: 369–425, 2024

Distributionally Robust Optimization

 

• C. Zhou* and G. Bayraksan, Effective Scenarios in Distributionally Robust Optimization with Wasserstein Distance, submitted
• 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
• 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, Forthcoming, 2022
• 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. 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, “Phi-divergence Constrained Ambiguous Stochastic Programs for Data-Driven Optimization”    [e-print] 
• 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

Monte Carlo Simulation-Based Methods for Stochastic Programming

 

• R. Kannan, G. Bayraksan and J.R. Luedtke, Data-Driven Sample Average Approximation with Covariate Information, Operations Research, Forthcoming, 2024
• Park, J.*, R. Stockbridge* and G. Bayraksan, Variance Reduction in Sequential Sampling for Stochastic Programming, Annals of Operations Research, 300(1): 171–204, 2021
• Bayraksan, G., An Improved Averaged Two-Replication Procedure with Latin Hypercube Sampling, Operations Research Letters, 46(2): 173–178, 2018
• 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
• 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
• 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
• 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
• 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     [preprint]
• 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
• Bayraksan, G. and D. P. Morton, “A Sequential Sampling Procedure for Stochastic Programming,” Operations Research, 59(4), 898–913, 2011
• Keller, B.* and G. Bayraksan, “Scheduling Jobs Sharing Multiple Resources under Uncertainty: A Stochastic Programming Approach,” IIE Transactions, 42, 16–30, 2010
• Bayraksan, G. and D. P. Morton, “Assessing Solution Quality in Stochastic Programs,” Mathematical Programming, 108,  495–514, 2006
• 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]
• 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
• 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
• 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

Energy and Water Resources Management