Robust optimization made easy with ROME We introduce ROME, an algebraic modeling toolbox for a class of robust optimization problems. ROME serves as an intermediate layer between the modeler and optimization solver engines, allowing modelers to express robust optimization problems in a mathematically meaningful way. In this paper, we discuss how ROME can be used to model (1) a service-constrained robust inventory management problem, (2) a project-crashing problem, and (3) a robust portfolio optimization problem. Through these modeling examples, we highlight the key features of ROME that allow it to expedite the modeling and subsequent numerical analysis of robust optimization problems. ROME is freely distributed for academic use at url{}.

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  1. Chassein, André; Goerigk, Marc: On the complexity of min-max-min robustness with two alternatives and budgeted uncertainty (2021)
  2. Chen, Xi; He, Simai; Jiang, Bo; Ryan, Christopher Thomas; Zhang, Teng: The discrete moment problem with nonconvex shape constraints (2021)
  3. Chen, Yannan; Sun, Hailin; Xu, Huifu: Decomposition and discrete approximation methods for solving two-stage distributionally robust optimization problems (2021)
  4. Han, Biao; Shang, Chao; Huang, Dexian: Multiple kernel learning-aided robust optimization: learning algorithm, computational tractability, and usage in multi-stage decision-making (2021)
  5. Rahal, Said; Papageorgiou, Dimitri J.; Li, Zukui: Hybrid strategies using linear and piecewise-linear decision rules for multistage adaptive linear optimization (2021)
  6. Saif, Ahmed; Delage, Erick: Data-driven distributionally robust capacitated facility location problem (2021)
  7. Shang, Chao; Ding, Steven X.; Ye, Hao: Distributionally robust fault detection design and assessment for dynamical systems (2021)
  8. Caunhye, Aakil M.; Aydin, Nazli Yonca; Duzgun, H. Sebnem: Robust post-disaster route restoration (2020)
  9. de Klerk, Etienne; Kuhn, Daniel; Postek, Krzysztof: Distributionally robust optimization with polynomial densities: theory, models and algorithms (2020)
  10. Georghiou, Angelos; Tsoukalas, Angelos; Wiesemann, Wolfram: A primal-dual lifting scheme for two-stage robust optimization (2020)
  11. Mazahir, Shumail; Ardestani-Jaafari, Amir: Robust global sourcing under compliance legislation (2020)
  12. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  13. Mittal, Areesh; Gokalp, Can; Hanasusanto, Grani A.: Robust quadratic programming with mixed-integer uncertainty (2020)
  14. Rockafellar, R. Tyrrell: Risk and utility in the duality framework of convex analysis (2020)
  15. Subramanyam, Anirudh; Gounaris, Chrysanthos E.; Wiesemann, Wolfram: (K)-adaptability in two-stage mixed-integer robust optimization (2020)
  16. Xie, Weijun: Tractable reformulations of two-stage distributionally robust linear programs over the type-( \infty) Wasserstein ball (2020)
  17. Arai, Takuji; Asano, Takao; Nishide, Katsumasa: Optimal initial capital induced by the optimized certainty equivalent (2019)
  18. Blanchet, Jose; Lam, Henry; Tang, Qihe; Yuan, Zhongyi: Robust actuarial risk analysis (2019)
  19. Blanchet, Jose; Murthy, Karthyek: Quantifying distributional model risk via optimal transport (2019)
  20. Carlsson, John Gunnar; Wang, Ye: Distributions with maximum spread subject to Wasserstein distance constraints (2019)

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