10.4230/OASICS.ATMOS.2007.1176
Fischetti, Matteo
Matteo
Fischetti
Zanette, Arrigo
Arrigo
Zanette
Salvagnin, Domenico
Domenico
Salvagnin
10. Fast Approaches to Robust Railway Timetabling
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
2007
Article
Train timetabling
Robust Optimization
Stochastic Programming
Computational Experiments
Liebchen, Christian
Christian
Liebchen
Ahuja, Ravindra K.
Ravindra K.
Ahuja
Mesa, Juan A.
Juan A.
Mesa
2007
2007-11-06
2007-11-06
2007-11-06
en
urn:nbn:de:0030-drops-11762
10.4230/OASIcs.ATMOS.2007
978-3-939897-04-0
2190-6807
10.4230/OASIcs.ATMOS.2007
OASIcs, Volume 7, ATMOS 2007
7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07)
2012
7
11
142
157
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Liebchen, Christian
Christian
Liebchen
Ahuja, Ravindra K.
Ravindra K.
Ahuja
Mesa, Juan A.
Juan A.
Mesa
2190-6807
Open Access Series in Informatics (OASIcs)
2007
7
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
16 pages
232757 bytes
application/pdf
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
info:eu-repo/semantics/openAccess
The Train Timetabling Problem (TTP) consists in finding a train schedule on a railway network that satisfies some operational constraints and maximizes some profit function which counts for the effciency of the infrastructure usage. In practical cases, however, the maximization of the objective function is not enough and one calls for a robust solution that is capable of absorbing as much as possible delays/disturbances on the network. In this paper we propose and analyze computationally four different methods to find robust TTP solutions for the aperiodic (non cyclic) case, that combine Mixed Integer Programming (MIP) and ad-hoc Stochastic Programming/Robust Optimization techniques. We compare
computationally the effectiveness and practical applicability of the four techniques under investigation on real-world test cases from the Italian railway company (Trenitalia). The outcome is that two of the proposed techniques are very fast and provide robust solutions of comparable quality with respect to the standard (but very time consuming) Stochastic
Programming approach.
OASIcs, Vol. 7, 7th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS'07), pages 142-157