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 41 articles )

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  1. Bertsimas, Dimitris; Georghiou, Angelos: Binary decision rules for multistage adaptive mixed-integer optimization (2018)
  2. Chassein, André; Goerigk, Marc: Compromise solutions for robust combinatorial optimization with variable-sized uncertainty (2018)
  3. Gauvin, Charles; Delage, Erick; Gendreau, Michel: A stochastic program with time series and affine decision rules for the reservoir management problem (2018)
  4. Karimi, Mehdi; Moazeni, Somayeh; Tunçel, Levent: A utility theory based interactive approach to robustness in linear optimization (2018)
  5. Xu, Huifu; Liu, Yongchao; Sun, Hailin: Distributionally robust optimization with matrix moment constraints: Lagrange duality and cutting plane methods (2018)
  6. Yanikoğlu, İhsan; Kuhn, Daniel: Decision rule bounds for two-stage stochastic bilevel programs (2018)
  7. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  8. Gauvin, Charles; Delage, Erick; Gendreau, Michel: Decision rule approximations for the risk averse reservoir management problem (2017)
  9. Govindan, Kannan; Fattahi, Mohammad; Keyvanshokooh, Esmaeil: Supply chain network design under uncertainty: a comprehensive review and future research directions (2017)
  10. Pflug, Georg Ch.; Timonina-Farkas, Anna; Hochrainer-Stigler, Stefan: Incorporating model uncertainty into optimal insurance contract design (2017)
  11. Sengupta, Raghu Nandan; Kumar, Rakesh: Robust and reliable portfolio optimization formulation of a chance constrained problem (2017)
  12. Zola, Enrica; Kassler, Andreas J.: Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty (2017)
  13. Cheng, Jianqiang; Leung, Janny; Lisser, Abdel: New reformulations of distributionally robust shortest path problem (2016)
  14. Gourtani, Arash; Xu, Huifu; Pozo, David; Nguyen, Tri-Dung: Robust unit commitment with $n-1$ security criteria (2016)
  15. Lam, Henry: Robust sensitivity analysis for stochastic systems (2016)
  16. Lorca, Álvaro; Sun, X. Andy; Litvinov, Eugene; Zheng, Tongxin: Multistage adaptive robust optimization for the unit commitment problem (2016)
  17. Mannor, Shie; Mebel, Ofir; Xu, Huan: Robust MDPs with $k$-rectangular uncertainty (2016)
  18. Sun, Hailin; Xu, Huifu: Convergence analysis for distributionally robust optimization and equilibrium problems (2016)
  19. Ben-Tal, Aharon; Den Hertog, Dick; Vial, Jean-Philippe: Deriving robust counterparts of nonlinear uncertain inequalities (2015)
  20. Bertsimas, Dimitris; Georghiou, Angelos: Design of near optimal decision rules in multistage adaptive mixed-integer optimization (2015)

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