Combinatorial and robust optimisation models and algorithms for railway applications

The following is a summary of the author’s Ph.D. thesis supervised by Alberto Caprara and Paolo Toth and defended on April 16, 2009 at the Università di Bologna. The thesis is written in English and is available from the author upon request. Railway systems represent a challenging area for operations research, especially when highly-complex and data-intensive applications, such as large-scale transportation networks, are at stake. One of the main issues concerns imperfect information. The classic notion of RobustOptimisation, as a way to represent and handle mathematically systems with not precisely known data, did not prove to be successfully applicable in the railway setting. For this reason a new paradigm has been defined recently in Liebchen et al. (2007): Recoverable robustness. Here we present our research on recoverable robust optimisation models for two important railway problems: Train platforming and Rolling stock planning.