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. Caunhye, Aakil M.; Aydin, Nazli Yonca; Duzgun, H. Sebnem: Robust post-disaster route restoration (2020)
  2. de Klerk, Etienne; Kuhn, Daniel; Postek, Krzysztof: Distributionally robust optimization with polynomial densities: theory, models and algorithms (2020)
  3. Georghiou, Angelos; Tsoukalas, Angelos; Wiesemann, Wolfram: A primal-dual lifting scheme for two-stage robust optimization (2020)
  4. Mazahir, Shumail; Ardestani-Jaafari, Amir: Robust global sourcing under compliance legislation (2020)
  5. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  6. Rockafellar, R. Tyrrell: Risk and utility in the duality framework of convex analysis (2020)
  7. Subramanyam, Anirudh; Gounaris, Chrysanthos E.; Wiesemann, Wolfram: (K)-adaptability in two-stage mixed-integer robust optimization (2020)
  8. Arai, Takuji; Asano, Takao; Nishide, Katsumasa: Optimal initial capital induced by the optimized certainty equivalent (2019)
  9. Blanchet, Jose; Lam, Henry; Tang, Qihe; Yuan, Zhongyi: Robust actuarial risk analysis (2019)
  10. Blanchet, Jose; Murthy, Karthyek: Quantifying distributional model risk via optimal transport (2019)
  11. Carlsson, John Gunnar; Wang, Ye: Distributions with maximum spread subject to Wasserstein distance constraints (2019)
  12. Chen, Zhi; Sim, Melvyn; Xu, Huan: Distributionally robust optimization with infinitely constrained ambiguity sets (2019)
  13. Chen, Zhi; Yu, Pengqian; Haskell, William B.: Distributionally robust optimization for sequential decision-making (2019)
  14. Georghiou, Angelos; Kuhn, Daniel; Wiesemann, Wolfram: The decision rule approach to optimization under uncertainty: methodology and applications (2019)
  15. Georghiou, Angelos; Tsoukalas, Angelos; Wiesemann, Wolfram: Robust dual dynamic programming (2019)
  16. Ghosh, Soumyadip; Lam, Henry: Robust analysis in stochastic simulation: computation and performance guarantees (2019)
  17. Goeva, Aleksandrina; Lam, Henry; Qian, Huajie; Zhang, Bo: Optimization-based calibration of simulation input models (2019)
  18. Gong, Zhaohua; Liu, Chongyang; Sun, Jie; Teo, Kok Lay: Distributionally robust (L_1)-estimation in multiple linear regression (2019)
  19. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)
  20. Ji, Ying; Qu, Shaojian; Chen, Fuxing: Environmental game modeling with uncertainties (2019)

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