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. de Klerk, Etienne; Kuhn, Daniel; Postek, Krzysztof: Distributionally robust optimization with polynomial densities: theory, models and algorithms (2020)
  2. Mazahir, Shumail; Ardestani-Jaafari, Amir: Robust global sourcing under compliance legislation (2020)
  3. Milz, Johannes; Ulbrich, Michael: An approximation scheme for distributionally robust nonlinear optimization (2020)
  4. Rockafellar, R. Tyrrell: Risk and utility in the duality framework of convex analysis (2020)
  5. Subramanyam, Anirudh; Gounaris, Chrysanthos E.; Wiesemann, Wolfram: (K)-adaptability in two-stage mixed-integer robust optimization (2020)
  6. Arai, Takuji; Asano, Takao; Nishide, Katsumasa: Optimal initial capital induced by the optimized certainty equivalent (2019)
  7. Blanchet, Jose; Lam, Henry; Tang, Qihe; Yuan, Zhongyi: Robust actuarial risk analysis (2019)
  8. Blanchet, Jose; Murthy, Karthyek: Quantifying distributional model risk via optimal transport (2019)
  9. Carlsson, John Gunnar; Wang, Ye: Distributions with maximum spread subject to Wasserstein distance constraints (2019)
  10. Chen, Zhi; Yu, Pengqian; Haskell, William B.: Distributionally robust optimization for sequential decision-making (2019)
  11. Georghiou, Angelos; Kuhn, Daniel; Wiesemann, Wolfram: The decision rule approach to optimization under uncertainty: methodology and applications (2019)
  12. Gong, Zhaohua; Liu, Chongyang; Sun, Jie; Teo, Kok Lay: Distributionally robust (L_1)-estimation in multiple linear regression (2019)
  13. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)
  14. Ji, Ying; Qu, Shaojian; Chen, Fuxing: Environmental game modeling with uncertainties (2019)
  15. Ji, Ying; Qu, Shaojian; Dai, Yeming: A new approach for worst-case regret portfolio optimization problem (2019)
  16. Li, Bin; Sun, Jie; Xu, Honglei; Zhang, Min: A class of two-stage distributionally robust games (2019)
  17. Luo, Fengqiao; Mehrotra, Sanjay: Decomposition algorithm for distributionally robust optimization using Wasserstein metric with an application to a class of regression models (2019)
  18. Maggioni, Francesca; Cagnolari, Matteo; Bertazzi, Luca: The value of the right distribution in stochastic programming with application to a Newsvendor problem (2019)
  19. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  20. Shafieezadeh-Abadeh, Soroosh; Kuhn, Daniel; Esfahani, Peyman Mohajerin: Regularization via mass transportation (2019)

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