ROME

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{http://www.robustopt.com}.


References in zbMATH (referenced in 124 articles )

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  1. Arslan, Ayşe N.; Detienne, Boris: Decomposition-based approaches for a class of two-stage robust binary optimization problems (2022)
  2. Embrechts, Paul; Schied, Alexander; Wang, Ruodu: Robustness in the optimization of risk measures (2022)
  3. Fu, Chenyi; Zhu, Ning; Ma, Shoufeng; Liu, Ronghui: A two-stage robust approach to integrated station location and rebalancing vehicle service design in bike-sharing systems (2022)
  4. Garatti, S.; Campi, M. C.: Risk and complexity in scenario optimization (2022)
  5. Kanno, Yoshihiro: Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization (2022)
  6. Kim, Byung-Cheol: Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment (2022)
  7. Lin, Fengming; Fang, Xiaolei; Gao, Zheming: Distributionally robust optimization. A review on theory and applications (2022)
  8. Li, Yongzhen; Li, Xueping; Shu, Jia; Song, Miao; Zhang, Kaike: A general model and efficient algorithms for reliable facility location problem under uncertain disruptions (2022)
  9. Nguyen, Viet Anh; Kuhn, Daniel; Esfahani, Peyman Mohajerin: Distributionally robust inverse covariance estimation: the Wasserstein shrinkage estimator (2022)
  10. Noyan, Nilay; Rudolf, Gábor; Lejeune, Miguel: Distributionally robust optimization under a decision-dependent ambiguity set with applications to machine scheduling and Humanitarian logistics (2022)
  11. Ramani, Sivaramakrishnan; Ghate, Archis: Robust Markov decision processes with data-driven, distance-based ambiguity sets (2022)
  12. Shehadeh, Karmel S.; Padman, Rema: Stochastic optimization approaches for elective surgery scheduling with downstream capacity constraints: models, challenges, and opportunities (2022)
  13. Zhang, Peiyu; Liu, Yankui; Yang, Guoqing; Zhang, Guoqing: A multi-objective distributionally robust model for sustainable last mile relief network design problem (2022)
  14. Ardestani-Jaafari, Amir; Delage, Erick: Linearized robust counterparts of two-stage robust optimization problems with applications in operations management (2021)
  15. Bansal, Ankit; Berg, Bjorn P.; Huang, Yu-Li: A value function-based approach for robust surgery planning (2021)
  16. Bertsimas, Dimitris; den Hertog, Dick; Pauphilet, Jean: Probabilistic guarantees in robust optimization (2021)
  17. Borrero, Juan S.; Lozano, Leonardo: Modeling defender-attacker problems as robust linear programs with mixed-integer uncertainty sets (2021)
  18. Chassein, André; Goerigk, Marc: On the complexity of min-max-min robustness with two alternatives and budgeted uncertainty (2021)
  19. Chen, Xi; He, Simai; Jiang, Bo; Ryan, Christopher Thomas; Zhang, Teng: The discrete moment problem with nonconvex shape constraints (2021)
  20. Chen, Yannan; Sun, Hailin; Xu, Huifu: Decomposition and discrete approximation methods for solving two-stage distributionally robust optimization problems (2021)

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