Below provides a List by Type. Publications by Topic

* indicates students advised.

**Preprints**

- R. Kannan, G. Bayraksan and J.R. Luedtke, Heteroscedasticity-aware residuals-based contextual stochastic optimization
- R. Kannan, G. Bayraksan and J.R. Luedtke, Residuals-Based Distributionally Robust Optimization with Covariate Information
- R. Kannan, G. Bayraksan and J.R. Luedtke, Data-Driven Sample Average Approximation with Covariate Information (recorded presentation (30-min))
- Park, J.*, and G. Bayraksan, A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty
- Love, D.K.* and G. Bayraksan, “Phi-divergence Constrained Ambiguous Stochastic Programs for Data-Driven Optimization” [e-print]

**Journal Articles and Tutorials**

- Park, J.*, R. Stockbridge* and G. Bayraksan, Variance Reduction in Sequential Sampling for Stochastic Programming,
, 300(1): 171–204, 2021*Annals of Operations Research* - Rahimian, H.*, G. Bayraksan and T. Homem-de-Mello, Controlling Risk and Demand Ambiguity in Newsvendor Models,
, 279(3): 854–868, 2019*European Journal of Operational Research* - Rahimian, H.*, G. Bayraksan and T. Homem-de-Mello, Identifying Effective Scenarios in Distributionally Robust Stochastic Programs with Variation Distance,
, 173(1–2): 393–430, 2019*Mathematical Programming*(

__Runner-Up, ICS Student Paper Prize__) - Bayraksan, G., An Improved Averaged Two-Replication Procedure with Latin Hypercube Sampling,
, 46(2): 173-178, 2018*Operations Research Letters* - Zhang, W.*, H. Rahimian* and G. Bayraksan, “Decomposition Algorithms for Risk-Averse Multistage Stochastic Programs with Application to Water Allocation under Uncertainty,”
, 28(3): 385-404, 2016*INFORMS Journal on Computing* - Stockbridge, R.* and G. Bayraksan, “Variance Reduction in Monte Carlo Sampling-Based Optimality Gap Estimators for Two-Stage Stochastic Linear Programming,”
, 64(2):407-43, 2016*Computational Optimization and Applications* - Lan, F.*, G. Bayraksan and K. Lansey, “Reformulation Linearization Technique based Branch-and-Reduce Approach to the Regional Water Supply System Design Problem,”
, 48(3): 454-475, 2016*Engineering Optimization* - Bayraksan, G. and D. Love*, “Data-Driven Stochastic Programming using Phi-Divergences,”
, INFORMS, Hanover, MD, 1–19, 2015*TutORials in Operations Research* - Love, D.* and G. Bayraksan, “Overlapping Batches for the Assessment of Solution Quality in Stochastic Programs,”
, 25(3), 2015*ACM Transactions on Modeling and Computer Simulation (TOMACS)* - Homem-de-Mello, T. and G. Bayraksan, “Monte Carlo Sampling-Based Methods for Stochastic Optimization
*,*”, 19(1): 56–85, 2014*Surveys in Operations Research and Management Science* - 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,”
, 113:1601–1613, 2014*Applied Energy* - Stockbridge, R.* and G. Bayraksan
**,**“A Probability Metrics Approach for Reducing the Bias of Optimality Gap Estimators in Two-Stage Stochastic Linear Programming*,*”, 147:107–131, 2013*Mathematical Programming* - 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) [paper]__INFORMS ENRE Best Publication Award in Environment and Sustainability__

- Bayraksan, G. and P. Pierre-Louis*, “Fixed-Width Sequential Stopping Rules for a Class of Stochastic Programs,”
, 22(4), 1518–1548, 2012 [paper]*SIAM Journal on Optimization* - Keller, B.* and G. Bayraksan, “Disjunctive Decomposition for Two-Stage Stochastic Integer Programs with GUB Constraints,”
, 24, 172–186, 2012 [paper]*INFORMS Journal on Computing* - Keller, B.* and G. Bayraksan, “Quantifying Operational Risk in Financial Institutions,’’
, 12(2), 100–113, 2012 (This has*INFORMS Transactions on Education*__won the__) [www]**INFORMS Best Case Study Award** - 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,”
, Volume 5, 102–122, INFORMS, Hannover, MD, 2009 [paper]*TutORials in Operations Research* - 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,”
, 108, 495–514, 2006 [paper]*Mathematical Programming*

**Book Chapters**

- Homem-de-Mello, T. and G. Bayraksan, “Stochastic Constraints and Variance Reduction Techniques,”
, edited by Michael C. Fu, pp. 245–276, 2015*Handbook of Simulation Optimization* - Bayraksan, G., “Solving Stochastic Programs,”
, 2011*Wiley Encyclopedia of Operations Research and Management Science* - 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]

**Selected Conference Proceedings**

- 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