Robust Optimization with Decision-Dependent Information Discovery

References

• Aghaei S, Azizi MJ, Vayanos P (2019) Learning optimal and fair decision trees for non-discriminative decision-making. Proc. 33rd AAAI Conf. Artificial Intelligence (AAAI, Washington, DC).Google Scholar
• Aghaei S, Gómez A, Vayanos P (2024) Strong optimal classification trees. Oper. Res., ePub ahead of print July 31, https://doi.org/10.1287/opre.2021.0034.Google Scholar
• Assavapokee T, Realff MJ, Ammons JC (2008a) Min-max regret robust optimization approach on interval data uncertainty. J. Optim. Theory Appl. 137:297–316.Crossref, Google Scholar
• Assavapokee T, Realff MJ, Ammons JC, Hong IH (2008b) Scenario relaxation algorithm for finite scenario-based min-max regret and min-max relative regret robust optimization. Comput. Oper. Res. 35(6):2093–2102.Crossref, Google Scholar
• Ayoub J, Poss M (2016) Decomposition for adjustable robust linear optimization subject to uncertainty polytope. Comput. Management Sci. 13(2):219–239.Crossref, Google Scholar
• Basciftci B, Ahmed S, Shen S (2021) Distributionally robust facility location problem under decision-dependent stochastic demand. Eur. J. Oper. Res. 292(2):548–561.Crossref, Google Scholar
• Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math. Oper. Res. 23(4):769–805.Link, Google Scholar
• Ben-Tal A, Nemirovski A (1999) Robust solutions of uncertain linear programs. Oper. Res. Lett. 25(1):1–13.Crossref, Google Scholar
• Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math. Programming 88:411–424.Crossref, Google Scholar
• Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization, Princeton Series in Applied Mathematics (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Ben-Tal A, Goryashko A, Guslitzer E, Nemirovski A (2004) Adjustable robust solutions of uncertain linear programs. Math. Programming 99(2):351–376.Crossref, Google Scholar
• Bertsimas D, Caramanis C (2010) Finite adaptability for linear optimization. IEEE Trans. Automatic Control 55(12):2751–2766.Crossref, Google Scholar
• Bertsimas D, Dunn J (2017) Optimal classification trees. Machine Learn. 106(7):1039–1082.Crossref, Google Scholar
• Bertsimas D, Dunning I (2016) Multistage robust mixed-integer optimization with adaptive partitions. Oper. Res. 64(4):980–998.Link, Google Scholar
• Bertsimas D, Georghiou A (2015) Design of near optimal decision rules in multistage adaptive mixed-integer optimization. Oper. Res. 63(3):610–627.Link, Google Scholar
• Bertsimas D, Georghiou A (2018) Binary decision rules for multistage adaptive mixed-integer optimization. Math. Programming 167(2):395–433.Crossref, Google Scholar
• Bertsimas D, Goyal V (2012) On the power and limitations of affine policies in two-stage adaptive optimization problems. Math. Programming 134:491–531.Crossref, Google Scholar
• Bertsimas D, O’Hair A (2013) Learning preferences under noise and loss aversion: An optimization approach. Oper. Res. 61(5):1190–1199.Link, Google Scholar
• Bertsimas D, Shtern S (2018) A scalable algorithm for two-stage adaptive linear optimization. Technical report, Operations Research Center, MIT, Cambridge, MA.Google Scholar
• Bertsimas D, Sim M (2004) The price of robustness. Oper. Res. 52(1):35–53.Link, Google Scholar
• Bertsimas D, Vayanos P (2017) Data-driven learning in dynamic pricing using adaptive robust optimization. Optimi. Online (October 11), https://optimization-online.org/2014/10/4595/.Google Scholar
• Bertsimas D, Iancu D, Parrilo P (2011) A hierarchy of near-optimal policies for multi-stage adaptive optimization. IEEE Trans. Automatic Control 56(12):2809–2824.Crossref, Google Scholar
• Bertsimas D, Pachamanova D, Sim M (2004) Robust linear optimization under general norms. Oper. Res. Lett. 32(6):510–516.Crossref, Google Scholar
• Bertsimas D, Delarue A, Jaillet P, Martin S (2019) The price of interpretability. Preprint, submitted July 8, https://arxiv.org/abs/1907.03419.Google Scholar
• Boutilier C, Sandholm T, Shields R (2004) Eliciting bid taker non-price preferences in (combinatorial) auctions. Proc. 19th Conf. Artificial Intelligence (ACM, New York), 204–211.Google Scholar
• Chassein A, Goerigk M, Kurtz J, Poss M (2019) Faster algorithms for min-max-min robustness for combinatorial problems with budgeted uncertainty. Eur. J. Oper. Res. 279(2):308–319.Crossref, Google Scholar
• Chen B, Wang J, Wang L, He Y, Wang Z (2014) Robust optimization for transmission expansion planning: Minimax cost vs. minimax regret. IEEE Trans. Power Systems 29(6):3069–3077.Crossref, Google Scholar
• Colvin M, Maravelias CT (2008) A stochastic programming approach for clinical trial planning in new drug development. Comput. Chemical Engrg. 32(11):2626–2642.Crossref, Google Scholar
• Colvin M, Maravelias CT (2010) Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming. Eur. J. Oper. Res. 203(1):205–215.Crossref, Google Scholar
• Feillet D, Gendreau M, Medaglia AL, Walteros JL (2010) A note on branch-and-cut-and-price. Oper. Res. Lett. 38(5):346–353.Crossref, Google Scholar
• Fischetti M, Vigo D (1997) A branch-and-cut algorithm for the resource-constrained minimum-weight arborescence problem. Networks 29(1):55–67.Crossref, Google Scholar
• Goel V, Grossman IE (2004) A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves. Comput. Chemical Engrg. 28(8):1409–1429.Crossref, Google Scholar
• Goel V, Grossman IE (2006) A class of stochastic programs with decision dependent uncertainty. Math. Programming 108(2):355–394.Crossref, Google Scholar
• Goel V, Grossman IE, El-Bakry AA, Mulkay EL (2006) A novel branch and bound algorithm for optimal development of gas fields under uncertainty in reserves. Comput. Chemical Engrg. 30(6–7):1076–1092.Crossref, Google Scholar
• Gupta V, Grossmann IE (2011) Solution strategies for multistage stochastic programming with endogenous uncertainties. Comput. Chemical Engrg. 35(11):2235–2247.Crossref, Google Scholar
• Gupta A, Nagarajan V, Singla S (2016) Algorithms and adaptivity gaps for stochastic probing. Proc. Annual ACM-SIAM Symposium Discrete Algorithms, vol. 3 (ACM, New York).Google Scholar
• Gupta A, Nagarajan V, Singla S (2017) Adaptivity gaps for stochastic probing: Submodular and XOS functions. Proc. Annual ACM-SIAM Symposium Discrete Algorithms (ACM, New York).Google Scholar
• Gupte A, Ahmed S, Dey SS, Cheon MS (2017) Relaxations and discretizations for the pooling problem. J. Global Optim. 67:631–669.Crossref, Google Scholar
• Hanasusanto GA, Kuhn D, Wiesemann W (2015) K-adaptability in two-stage robust binary programming. Oper. Res. 63(4):877–891.Link, Google Scholar
• Hanasusanto GA, Kuhn D, Wiesemann W (2016) K-adaptability in two-stage distributionally robust binary programming. Oper. Res. Lett. 44(1):6–11.Crossref, Google Scholar
• Jiang R, Wang J, Zhang M, Guan Y (2013) Two-stage minimax regret robust unit commitment. IEEE Trans. Power Systems 28(3):2271–2282.Crossref, Google Scholar
• Jonsbråten TW (1998) Optimization models for petroleum field exploitation. PhD thesis, Norwegian School of Economics and Business Administration, Bergen, Norway.Google Scholar
• Jonsbråten TW, Wets RB, Woodruff DL (1998) A class of stochastic programs with decision dependent random elements. Ann. Oper. Res. 82:83–106.Crossref, Google Scholar
• Koç A, Morton DP (2015) Prioritization via stochastic optimization. Management Sci. 61(3):586–603.Link, Google Scholar
• KPSAM (2015) Kidney-Pancreas Simulated Allocation Model User Guide version 2015. https://www.srtr.org/media/1202/kpsam.pdf.Google Scholar
• Löbel A (1998) Vehicle scheduling in public transit and Lagrangean pricing. Management Sci. 44(12 Part 1):1637–1649.Link, Google Scholar
• Lorca A, Sun XA (2016) Multistage robust unit commitment with dynamic uncertainty sets and energy storage. IEEE Trans. Power Systems 32(3):1678–1688.Crossref, Google Scholar
• Luo F, Mehrotra S (2020) Distributionally robust optimization with decision dependent ambiguity sets. Optim. Lett. 14(8):2565–2594.Crossref, Google Scholar
• Mamer JW, McBride RD (2000) Decomposition-based pricing procedure for large-scale linear programs: An application to the linear multicommodity flow problem. Management Sci. 46(5):693–709.Link, Google Scholar
• McCarthy SM, Laan CM, Wang K, Vayanos P, Sinha A, Tambe M (2018) The price of usability: Designing operationalizable strategies for security games. Proc. Twenty-Seventh Internat. Joint Conf. Artificial Intelligence, IJCAI-18 (IJCAI, California), 454–460.Google Scholar
• Muter I, Birbil SI, Bülbül K (2013) Simultaneous column-and-row generation for large-scale linear programs with column-dependent-rows. Math. Programming 142:47–82.Crossref, Google Scholar
• Muter I, Birbil I, Bülbül K (2018) Benders decomposition and column-and-row generation for solving large-scale linear programs with column-dependent-rows. Eur. J. Oper. Res. 264(1):29–45.Crossref, Google Scholar
• Ng TS (2013) Robust regret for uncertain linear programs with application to co-production models. Eur. J. Oper. Res. 227(3):483–493.Crossref, Google Scholar
• Ning C, You F (2018) Adaptive robust optimization with minimax regret criterion: Multiobjective optimization framework and computational algorithm for planning and scheduling under uncertainty. Comput. Chemical Engrg. 108:425–447.Crossref, Google Scholar
• Nohadani O, Roy A (2017) Robust optimization with time-dependent uncertainty in radiation therapy. IISE Trans. Healthcare Systems Engrg. 7(2):81–92.Crossref, Google Scholar
• Nohadani O, Sharma K (2018) Optimization under decision-dependent uncertainty. SIAM J. Optim. 28(2):1773–1795.Crossref, Google Scholar
• Noyan N, Rudolf G, Lejeune M (2022) Distributionally robust optimization under a decision-dependent ambiguity set with applications to machine scheduling and humanitarian logistics. INFORMS J. Comput. 34(2):729–751.Link, Google Scholar
• Postek K, Den Hertog D (2016) Multistage adjustable robust mixed-integer optimization via iterative splitting of the uncertainty set. INFORMS J. Comput. 28(3):553–574.Link, Google Scholar
• Poursoltani M, Delage E (2019) Adjustable robust optimization reformulations of two-stage worst-case regret minimization problems. Optim. Online (July 22), https://optimization-online.org/2019/07/7303/.Google Scholar
• Rahmattalabi A, Vayanos P, Fulginiti A, Rice E, Wilder B, Yadav A, Tambe M (2019) Exploring algorithmic fairness in robust graph covering problems. Proc. 33rd Conf. Neural Inform. Processing Systems (NeurIPS) (Curran Associates Inc., Red Hook, NY).Google Scholar
• Sadykov R, Vanderbeck F (2011) Column generation for extended formulations. Electronic Notes Discrete Math. 37:357–362.Crossref, Google Scholar
• Shapiro A (2017) Interchangeability principle and dynamic equations in risk averse stochastic programming. Oper. Res. Lett. 45(4):377–381.Crossref, Google Scholar
• Singla S (2018) Combinatorial optimization under uncertainty: Probing and stopping-time algorithms. PhD thesis, Carnegie Mellon University, Pittsburgh.Google Scholar
• Solak S, Clarke JP, Johnson EL, Barnes ER (2010) Optimization of R&D project portfolios under endogenous uncertainty. Eur. J. Oper. Res. 207(1):420–433.Crossref, Google Scholar
• Spacey SA, Wiesemann W, Kuhn D, Luk W (2012) Robust software partitioning with multiple instantiation. INFORMS J. Comput. 24(3):500–515.Link, Google Scholar
• Subramanyam A, Gounaris CE, Wiesemann W (2020) K-adaptability in two-stage mixed-integer robust optimization. Math. Programming Comput. 12:193–224.Crossref, Google Scholar
• Toubia O, Hauser J, Garcia R (2007) Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Sci. 26(5):596–610.Link, Google Scholar
• Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41(1):116–131.Crossref, Google Scholar
• Toubia O, Simester DI, Hauser JR, Dahan E (2003) Fast polyhedral adaptive conjoint estimation. Marketing Sci. 22(3):273–303.Link, Google Scholar
• Tsoukalas A, Mitsos A (2014) Multivariate McCormick relaxations. J. Global Optim. 59:633–662.Crossref, Google Scholar
• Valério De Carvalho JM (1999) Exact solution of bin-packing problems using column generation and branch-and-bound. Ann. Oper. Res. 86:629–659.Crossref, Google Scholar
• Vayanos P, Jin Q, Elissaios G (2023) ROC++: Robust optimization in C++. INFORMS J. Comput. 34(6):2873–2888.Link, Google Scholar
• Vayanos P, Kuhn D, Rustem B (2011) Decision rules for information discovery in multi-stage stochastic programming. Proc. 50th IEEE Conf. Decision Control (IEEE, Piscataway, NJ), 7368–7373.Google Scholar
• Vayanos P, Kuhn D, Rustem B (2012) A constraint sampling approach for multi-stage robust optimization. Automatica 48(3):459–471.Crossref, Google Scholar
• Vayanos P, Ye Y, McElfresh D, Dickerson J, Rice E (2022) Robust active preference elicitation. Preprint, submitted March 4, https://arxiv.org/abs/2003.01899.Google Scholar
• Wolfe R, Leichtman A, McCullough K, Rodgers A (2009) Final analyses for data requests from the OPTN Kidney Transplantation Committee Meeting of August 24, 2009. Technical report, Arbor Research/University of Michigan, Ann Arbor, MI.Google Scholar
• Xu G, Burer S (2018) A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides. Comput. Optim. Appl. 70(1):33–59.Crossref, Google Scholar
• Yu X, Shen S (2022) Multistage distributionally robust mixed-integer programming with decision-dependent moment-based ambiguity sets. Math. Programming 196(1–2):1025–1064.Crossref, Google Scholar
• Zeng B, Zhao L (2013) Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 41(5):457–461.Crossref, Google Scholar
• Zhang M (2011) Two-stage minimax regret robust uncapacitated lot-sizing problems with demand uncertainty. Oper. Res. Lett. 39(5):342–345.Crossref, Google Scholar
• Zhang X, Kamgarpour M, Georghiou A, Goulart P, Lygeros J (2017) Robust optimal control with adjustable uncertainty sets. Automatica 75(Supplement C):249–259.Crossref, Google Scholar
• Zhen J, Den Hertog D, Sim M (2018) Adjustable robust optimization via Fourier-Motzkin elimination. Oper. Res. 66(4):1086–1100.Link, Google Scholar