We present a new concept for optimization under uncertainty: recoverable robustness. A solution is recovery robust if it can be recovered by limited means in all likely scenarios. Specializing the general concept to linear programming we can show that recoverable robustness combines the flexibility of stochastic programming with the tractability and performances guarantee of the classical robust approach. We exemplify recoverable robustness in delay resistant, periodic and aperiodic timetabling problems, and train platforming. © 2009 Springer-Verlag Berlin Heidelberg.
Liebchen, C., Lübbecke, M., Möhring, R., & Stiller, S. (2009). The concept of recoverable robustness, linear programming recovery, and railway applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5868 LNCS, pp. 1–27). https://doi.org/10.1007/978-3-642-05465-5_1