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{}.

References in zbMATH (referenced in 30 articles )

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  1. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  2. Cheng, Jianqiang; Leung, Janny; Lisser, Abdel: New reformulations of distributionally robust shortest path problem (2016)
  3. Gourtani, Arash; Xu, Huifu; Pozo, David; Nguyen, Tri-Dung: Robust unit commitment with $n-1$ security criteria (2016)
  4. Lam, Henry: Robust sensitivity analysis for stochastic systems (2016)
  5. Lorca, Álvaro; Sun, X.Andy; Litvinov, Eugene; Zheng, Tongxin: Multistage adaptive robust optimization for the unit commitment problem (2016)
  6. Mannor, Shie; Mebel, Ofir; Xu, Huan: Robust MDPs with $k$-rectangular uncertainty (2016)
  7. Sun, Hailin; Xu, Huifu: Convergence analysis for distributionally robust optimization and equilibrium problems (2016)
  8. Ben-Tal, Aharon; Den Hertog, Dick; Vial, Jean-Philippe: Deriving robust counterparts of nonlinear uncertain inequalities (2015)
  9. Bertsimas, Dimitris; Georghiou, Angelos: Design of near optimal decision rules in multistage adaptive mixed-integer optimization (2015)
  10. Desmettre, Sascha; Korn, Ralf; Ruckdeschel, Peter; Seifried, Frank Thomas: Robust worst-case optimal investment (2015)
  11. Georghiou, Angelos; Wiesemann, Wolfram; Kuhn, Daniel: Generalized decision rule approximations for stochastic programming via liftings (2015)
  12. Hanasusanto, Grani A.; Kuhn, Daniel; Wallace, Stein W.; Zymler, Steve: Distributionally robust multi-item newsvendor problems with multimodal demand distributions (2015)
  13. Hanasusanto, Grani A.; Kuhn, Daniel; Wiesemann, Wolfram: $K$-adaptability in two-stage robust binary programming (2015)
  14. Meng, Fanwen; Qi, Jin; Zhang, Meilin; Ang, James; Chu, Singfat; Sim, Melvyn: A robust optimization model for managing elective admission in a public hospital (2015)
  15. Wang, Xuan; Zhang, Jiawei: Process flexibility: a distribution-free bound on the performance of $k$-chain (2015)
  16. Analui, Bita; Pflug, Georg Ch.: On distributionally robust multiperiod stochastic optimization (2014)
  17. Gabrel, Virginie; Murat, Cécile; Thiele, Aurélie: Recent advances in robust optimization: an overview (2014)
  18. Ji, Ying; Wang, Tienan; Goh, Mark; Zhou, Yong; Zou, Bo: The worst-case discounted regret portfolio optimization problem (2014)
  19. Li, Xiaobo; Natarajan, Karthik; Teo, Chung-Piaw; Zheng, Zhichao: Distributionally robust mixed integer linear programs: persistency models with applications (2014)
  20. Ng, Tsan Sheng; Sy, Charlle Lee: A resilience optimization approach for workforce-inventory control dynamics under uncertainty (2014)

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