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. Arai, Takuji; Asano, Takao; Nishide, Katsumasa: Optimal initial capital induced by the optimized certainty equivalent (2019)
  2. Hughes, Martin; Goerigk, Marc; Wright, Michael: A largest empty hypersphere metaheuristic for robust optimisation with implementation uncertainty (2019)
  3. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  4. Ben-Ameur, Walid; Ouorou, Adam; Wang, Guanglei; Żotkiewicz, Mateusz: Multipolar robust optimization (2018)
  5. Bertsimas, Dimitris; Georghiou, Angelos: Binary decision rules for multistage adaptive mixed-integer optimization (2018)
  6. Chassein, André; Goerigk, Marc: Compromise solutions for robust combinatorial optimization with variable-sized uncertainty (2018)
  7. Chen, Ruidi; Paschalidis, Ioannis Ch.: A robust learning approach for regression models based on distributionally robust optimization (2018)
  8. Costa Santos, Marcio; Poss, Michael; Nace, Dritan: A perfect information lower bound for robust lot-sizing problems (2018)
  9. Gauvin, Charles; Delage, Erick; Gendreau, Michel: A stochastic program with time series and affine decision rules for the reservoir management problem (2018)
  10. Karimi, Mehdi; Moazeni, Somayeh; Tunçel, Levent: A utility theory based interactive approach to robustness in linear optimization (2018)
  11. Mohajerin Esfahani, Peyman; Kuhn, Daniel: Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations (2018)
  12. Wang, Guanglei; Hijazi, Hassan: Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches (2018)
  13. Xu, Huifu; Liu, Yongchao; Sun, Hailin: Distributionally robust optimization with matrix moment constraints: Lagrange duality and cutting plane methods (2018)
  14. Yanikoğlu, İhsan; Kuhn, Daniel: Decision rule bounds for two-stage stochastic bilevel programs (2018)
  15. de Ruiter, Frans J. C. T.; Ben-Tal, Aharon; Brekelmans, Ruud C. M.; den Hertog, Dick: Robust optimization of uncertain multistage inventory systems with inexact data in decision rules (2017)
  16. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  17. Gauvin, Charles; Delage, Erick; Gendreau, Michel: Decision rule approximations for the risk averse reservoir management problem (2017)
  18. Govindan, Kannan; Fattahi, Mohammad; Keyvanshokooh, Esmaeil: Supply chain network design under uncertainty: a comprehensive review and future research directions (2017)
  19. Pflug, Georg Ch.; Timonina-Farkas, Anna; Hochrainer-Stigler, Stefan: Incorporating model uncertainty into optimal insurance contract design (2017)
  20. Sengupta, Raghu Nandan; Kumar, Rakesh: Robust and reliable portfolio optimization formulation of a chance constrained problem (2017)

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